Jamie Hou

Zerve at AI and Big Data Expo Global 2025

Join Zerve at the AI & Big Data Expo Global 2025 on 5-6 February at Olympia, London. Visit us at Booth 72 and hear our CPO, Greg, speak on enterprise AI innovation (Day 1, 2:05 PM) and join the 9:55 AM panel on Gen AI's future.

<p>We are excited to announce that<a href="https://www.ai-expo.net/global/partners/zerve/" rel="noopener" target="_blank"> Zerve </a>will be exhibiting at the <a href="https://www.ai-expo.net/global/tickets-2/" rel="noopener" target="_blank"><strong>AI and Big Data Expo Global</strong>,</a> taking place on <strong>5-6 February 2025 at Olympia, London</strong>. As a leading event for <strong>Enterprise AI, Machine Learning, Data Ecosystems, and NLP</strong>, this conference attracts over <strong>7,000 industry professionals</strong> eager to explore the latest advancements in Artificial Intelligence and Big Data.</p>
<p>Our Chief Product Officer, <a href="https://www.linkedin.com/in/greg-michaelson/" rel="noopener" target="_blank"><strong>Greg Michaelson</strong></a>, will be representing Zerve with two key contributions to the event:</p>
<ul>
<li><strong><a href="https://www.ai-expo.net/global/agenda-2025/" rel="noopener" target="_blank">Day 1 Presentation (2:05 PM – 2:35 PM)</a>:</strong> Greg will deliver an insightful talk after the lunch break, sharing how Zerve is driving innovation in <strong>enterprise AI tools</strong> and enabling businesses to unlock the true potential of <strong>AI and Big Data</strong>.</li>
<li><strong><a href="https://www.ai-expo.net/global/agenda-2025/" rel="noopener" target="_blank">Panel Discussion (9:55 AM – 10:35 AM)</a>:</strong> Greg will join industry leaders on the panel discussion <strong>"The Next Decade of Gen AI Progress: A Look at What’s Coming and How to Prepare"</strong>, offering his perspective on how enterprises can stay ahead in the rapidly evolving AI landscape.</li>
</ul>
<p>This year’s <a href="https://www.ai-expo.net/global/speakers/" rel="noopener" target="_blank">speaker lineup</a> includes experts from leading organisations such as NVIDIA, Meta, Volvo, Pfizer, and more, covering critical topics in AI, including:</p>
<ul>
<li>How generative AI can positively augment workforce capabilities.</li>
<li>Ethical AI practices and strategies for securing AI-driven ecosystems.</li>
</ul>
<p>We are thrilled to be part of this incredible event and to connect with fellow innovators, thought leaders, and enterprises shaping the future of AI. <strong>Visit us at Booth 72</strong> to learn more about Zerve AI's enterprise solutions and how we can help your organization stay at the forefront of AI innovation.</p>
<p><strong>Want to join us as a VIP guest?</strong> Contact us to be part of this event as Zerve’s VIP guest. We’d love to provide you with <strong>complimentary event passes</strong>!<br>Reach out to <strong>Jamie Hou</strong> at <strong><a rel="noopener"><span>jamie</span><span>@zerve</span><span>.io</span></a></strong> for more details.</p>{{ include_custom_fonts({"InterCustom":["Medium","Regular","Semi Bold"]}) }}

<p>We are excited to announce that<a href="https://www.ai-expo.net/global/partners/zerve/" rel="noopener" target="_blank"> Zerve </a>will be exhibiting at the <a href="https://www.ai-expo.net/global/tickets-2/" rel="noopener" target="_blank"><strong>AI and Big Data Expo Global</strong>,</a> taking place on <strong>5-6 February 2025 at Olympia, London</strong>. As a leading event for <strong>Enterprise AI, Machine Learning, Data Ecosystems, and NLP</strong>, this conference attracts over <strong>7,000 industry professionals</strong> eager to explore the latest advancements in Artificial Intelligence and Big Data.</p>


Greg Michaelson

Zerve blog Mastering Model Validation: Effective Train-Test Splits and Cross-Validation Strategies

Mastering Model Validation: Effective Train-Test Splits and Cross-Validation Strategies

Learn effective model validation techniques like train-test splits and cross-validation strategies to build robust predictive models. Discover AI best practices and insights tailored for high-stakes industries like banking.

<p>As an experienced data scientist, I’ve learned that one of the most critical steps in building robust predictive models is proper <a href="https://www.geeksforgeeks.org/what-is-model-validation-and-why-is-it-important/" rel="noopener" target="_blank">model validation</a>. Whether you're applying <a href="https://www.ibm.com/think/topics/supervised-learning" rel="noopener" target="_blank">supervised learning</a> techniques to classify loan applications or using <a href="https://cloud.google.com/discover/what-is-unsupervised-learning" rel="noopener" target="_blank">unsupervised learning</a> to detect anomalies, validation is the cornerstone of ensuring your model generalizes well to unseen data.</p>
<p>In this article, I’ll walk you through the fundamentals of train-test splits and <a href="https://docs.aws.amazon.com/machine-learning/latest/dg/cross-validation.html" rel="noopener" target="_blank">cross-validation</a> strategies, share insights into AI best practices, and discuss their application in the banking sector—a field where accuracy and reliability are paramount.</p>
<hr>
<h2 style="font-size: 36px;">Why Model Validation Matters</h2>
<p>Model validation is more than just a step in the data science best practice workflow; it’s the mechanism that protects us from overfitting and ensures our models deliver meaningful results in real-world applications. Without it, even the most sophisticated model can become a liability.<br>I remember working with a banking client on a project to predict loan defaults. Initially, the model performed exceptionally well on the training data, with over 95% accuracy. However, when tested on new data, its accuracy plummeted to around 60%. The culprit? <a href="https://docs.aws.amazon.com/machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html" rel="noopener" target="_blank">Overfitting</a> due to improper validation. This experience taught me the value of robust validation techniques like train-test splits and cross-validation.</p>
<h2 style="font-size: 36px;">The Fundamentals of Train-Test Splits</h2>
<p style="font-size: 14px;">The train-test split is the simplest and most widely used method of model validation. By dividing the dataset into two parts—a training set and a testing set—we evaluate how well the model generalizes to unseen data.<br>Here’s how I typically approach a train-test split:</p>
<pre>import pandas as pd<br>from sklearn.model_selection import train_test_split<br>from sklearn.ensemble import RandomForestClassifier<br><br># Example dataset: banking transactions for fraud detection<br>data = pd.DataFrame({<br>&nbsp; &nbsp; 'transaction_amount': [100, 200, 150, 400, 250, 300, 450],<br>&nbsp; &nbsp; 'transaction_time': [5, 12, 8, 20, 15, 25, 30],<br>&nbsp; &nbsp; 'is_fraud': [0, 1, 0, 1, 0, 1, 0]<br>})<br><br># Features and target<br>X = data[['transaction_amount', 'transaction_time']]<br>y = data['is_fraud']<br><br># Train-test split<br>X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)<br><br># Training a simple model<br>model = RandomForestClassifier(random_state=42)<br>model.fit(X_train, y_train)<br><br># Evaluating the model<br>accuracy = model.score(X_test, y_test)<br>print(f"Test Accuracy: {accuracy:.2f}")</pre>
<p style="font-size: 14px;">This approach provides a quick and effective way to assess model performance. However, its simplicity can also be a limitation, as the results may vary depending on how the data is split. That’s where cross-validation comes in.</p>
<h2 style="font-size: 36px;">Cross-Validation for Robust Model Assessment</h2>
<p style="font-size: 14px;">Cross-validation divides the data into multiple subsets (folds), training the model on some folds while testing on others. This method gives a more comprehensive evaluation of model performance, reducing variability caused by random splits.<br>One of my favorite techniques is k-fold cross-validation, particularly when working with critical financial applications like credit scoring models. Here’s how it works:</p>
<pre style="font-size: 14px;">from sklearn.model_selection import cross_val_score<br>from sklearn.ensemble import RandomForestClassifier<br><br># Using the same dataset as above<br>model = RandomForestClassifier(random_state=42)<br><br># Performing k-fold cross-validation<br>scores = cross_val_score(model, X, y, cv=5) &nbsp;# 5-fold cross-validation<br>print(f"Cross-Validation Scores: {scores}")<br>print(f"Mean CV Accuracy: {scores.mean():.2f}")</pre>
<p style="font-size: 14px;">By using 10-fold cross-validation, we ensured the model was robust across diverse data subsets, giving the client confidence in its reliability.</p>
<p style="font-size: 14px;">Another challenge I faced was dealing with imbalanced datasets, which are common in banking. For instance, in fraud detection, the number of fraudulent transactions is often much smaller than the number of legitimate ones. To address this, I’ve used techniques like stratified cross-validation, which ensures each fold in the validation process maintains the same class distribution as the original dataset.<br>Here’s an example:</p>
<pre style="font-size: 14px;">from sklearn.model_selection import StratifiedKFold<br><br># Stratified k-fold cross-validation<br>stratified_kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)<br><br>for train_index, test_index in stratified_kfold.split(X, y):<br>&nbsp; &nbsp; X_train, X_test = X.iloc[train_index], X.iloc[test_index]<br>&nbsp; &nbsp; y_train, y_test = y.iloc[train_index], y.iloc[test_index]<br>&nbsp; &nbsp; model.fit(X_train, y_train)<br>&nbsp; &nbsp; accuracy = model.score(X_test, y_test)<br>&nbsp; &nbsp; print(f"Fold Accuracy: {accuracy:.2f}")</pre>
<p style="font-size: 14px;">This approach ensures the model is evaluated on data that reflects real-world conditions, making it more reliable when deployed.</p>
<h2 style="font-size: 36px;">Best Practices for Model Validation</h2>
<p style="font-size: 14px;">Through my experiences, I’ve identified a few AI best practices for effective model validation:<br>Always perform multiple validation techniques, such as train-test splits and cross-validation, to ensure consistent results.<br>Use stratified sampling when working with imbalanced datasets to maintain the integrity of the class distribution.<br>Regularly evaluate your validation strategy as your dataset evolves or grows.<br>These practices not only improve model performance but also instill confidence in its predictions, especially in high-stakes industries like banking.</p>
<h2 style="font-size: 36px;">Conclusion</h2>
<p style="font-size: 14px;">In my work, I’ve seen firsthand how techniques like train-test splits, k-fold cross-validation, and stratified sampling can make the difference between a model that works and one that fails. Validation is not just a step—it’s a practice. By adhering to data science best practices, you’ll build models that not only perform well but also earn the trust of stakeholders.</p>
<p style="font-size: 14px;">&nbsp;Try <a href="https://www.zerve.ai/resource/free-trial" rel="noopener" target="_blank">Zerve </a>Now!</p>{{ include_custom_fonts({"InterCustom":["Medium","Regular","Semi Bold"],"PPTelegraf":["Bold","Regular"]}) }}

<p>As an experienced data scientist, I’ve learned that one of the most critical steps in building robust predictive models is proper <a href="https://www.geeksforgeeks.org/what-is-model-validation-and-why-is-it-important/" rel="noopener" target="_blank">model validation</a>. Whether you're applying <a href="https://www.ibm.com/think/topics/supervised-learning" rel="noopener" target="_blank">supervised learning</a> techniques to classify loan applications or using <a href="https://cloud.google.com/discover/what-is-unsupervised-learning" rel="noopener" target="_blank">unsupervised learning</a> to detect anomalies, validation is the cornerstone of ensuring your model generalizes well to unseen data.</p>
<p></p>

Optimizing Performance: A Hands-On Guide to Hyperparameter Tuning

Discover how hyperparameter tuning transforms model performance in data science. Explore grid search, random search, and Bayesian optimization techniques with real-world examples from industries like banking. Learn AI best practices for tuning and validation.

<p style="line-height: 1.15;"><span style="font-size: 16px;">When it comes to learning data science or learning AI, one of the most impactful techniques for improving model performance is <a href="https://aws.amazon.com/what-is/hyperparameter-tuning/" rel="noopener" target="_blank">hyperparameter tuning</a>. Hyperparameters are the configuration settings that control the training process of a model but are not updated during training itself. These include values like the number of trees in a <a href="https://en.wikipedia.org/wiki/Random_forest" rel="noopener" target="_blank">random forest</a>, the <a href="https://en.wikipedia.org/wiki/Learning_rate" rel="noopener" target="_blank">learning rate</a> for <a href="https://en.wikipedia.org/wiki/Gradient_boosting" rel="noopener" target="_blank">gradient boosting</a>, or the k value in <a href="https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm" rel="noopener" target="_blank">k-nearest neighbors</a>.</span></p>

Key Trends in Data Science and AI Projects: Efficiency, Governance, and Collaboration

Explore the key trends shaping data science and AI in 2025, including efficiency, governance, and bridging the gap between data science and production. Discover insights from Gartner, MIT, and top industry events, and learn how Zerve empowers organizations to stay ahead.

<p>The fields of Data Science and Artificial Intelligence (AI) continue to evolve rapidly, presenting organizations with transformative opportunities and emerging challenges. Insights from recent industry events such as FIMA Europe, the Analytics Institute Summit in Dublin, and Big Data London, combined with findings from the MIT article "<a href="https://sloanreview.mit.edu/article/five-key-trends-in-ai-and-data-science-for-2024/" rel="noopener" target="_blank">Five Key Trends in AI and Data Science for 2024</a>" and <a href="https://www.gartner.com/en/data-analytics/topics/data-governance" rel="noopener" target="_blank">Gartner’s research</a>, highlight the critical priorities shaping the future of data science and AI projects in 2025 and beyond.<br>Recurring themes such as efficient workflows, robust governance, and the seamless transition from prototypes to production have emerged as essential focus areas for organizations striving to stay competitive.</p>
<h3>1. Efficiency: Driving Speed and Scalability in Data Science</h3>
<p>Efficiency is a top priority for organizations seeking to accelerate time-to-value without compromising quality.</p>
<h4>Key Challenges Identified</h4>
<p>At the Analytics Summit in Dublin, attendees highlighted several bottlenecks, including:</p>
<ul>
<li>Siloed Data Sources: These hinder collaboration and prevent holistic insights.</li>
<li>Diverse Tooling and Programming Languages: Inconsistent tools create inefficiencies and complexity.</li>
<li>Critical Handovers: Transitions between data scientists and engineers often result in delays and workflow disruptions.</li>
</ul>
<h4>Industrializing Data Science</h4>
<p>As the MIT article underscores, data science is transitioning from an artisanal to an industrialized approach. Organizations are increasingly investing in platforms, MLOps systems, and automation to boost productivity and broaden participation in data science workflows. Tools that enable the reuse of datasets, features, and models are key to scaling data operations efficiently.</p>
<h4>Zerve’s Role in Efficiency</h4>
<p>Zerve is designed to eliminate inefficiencies and support diverse use cases, including building data pipelines and fine-tuning large language models (LLMs). By streamlining workflows, Zerve enables teams to:</p>
<ul>
<li>Accelerate deployment cycles.</li>
<li>Scale operations without sacrificing quality.</li>
<li>Deliver faster, more impactful results.</li>
</ul>
<p>One of our media streaming customers leveraged Zerve to unify their platform, reducing friction and improving customer engagement strategies. This allowed them to accelerate personalized recommendations, enhancing their competitive edge.</p>
<hr>
<h3 data-pm-slice="1 1 []"><span>2. Governance: Building Trust and Ensuring Compliance</span></h3>
<p>Governance is critical for organizations managing sensitive data, especially in regulated industries like financial services, banking, and hedge funds.</p>
<h4>The Imperative for Governance in AI Development</h4>
<p>According to <a href="https://www.gartner.com/en/data-analytics/topics/data-governance" rel="noopener" target="_blank">Gartner’s report</a>, organizations that fail to integrate governance into their data and AI strategies risk inefficiencies and compliance violations. By 2025, Gartner predicts that 60% of organizations will adopt platforms with built-in <a href="https://www.ibm.com/topics/data-governance" rel="noopener" target="_blank">data governance</a>,<a href="https://www.ibm.com/topics/data-lineage" rel="noopener" target="_blank"> lineage</a>, and <a href="https://www.ibm.com/topics/data-observability" rel="noopener" target="_blank">observability</a> to ensure compliance and operational efficiency.<br><br>The MIT article further emphasizes that data quality and the ability to curate unstructured content are essential for unlocking the full potential of AI, especially generative AI. While 93% of surveyed organizations believe a robust data strategy is crucial for success, over half have not made significant changes to their processes.</p>
<h4>Zerve’s Role in Governance</h4>
<p>Zerve’s self-hosted solution (<a href="https://aws.amazon.com/marketplace/seller-profile?id=seller-bfwm5mq4awafy" rel="noopener" target="_blank">cloud</a> or on your server) offers:</p>
<ul>
<li>Security Controls: Protect sensitive information and prevent unauthorized access.</li>
<li>Data Lineage and Observability: Track and monitor data pipelines with full transparency.</li>
<li>Compliance Alignment: Integrate seamlessly with cloud services and data warehouse platforms while adhering to industry regulations.</li>
</ul>
<div class="hs-embed-wrapper hs-fullwidth-embed" data-service="youtube" data-responsive="true" style="position: relative; overflow: hidden; width: 100%; height: auto; padding: 0px; min-width: 256px; display: block; margin: auto;"><div class="hs-embed-content-wrapper"><div style="position: relative; overflow: hidden; max-width: 100%; padding-bottom: 75%; margin: 0px;"><iframe width="256" height="192" src="https://www.youtube.com/embed/j3L1ZXtjyZU?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen title="Highlights from FIMA Europe London 2024" style="position: absolute; top: 0px; left: 0px; width: 100%; height: 100%; border: none;"></iframe></div></div></div>
<p>This comprehensive approach enables organizations to innovate confidently while maintaining trust and meeting compliance standards.</p>
<hr>
<h3 data-pm-slice="1 1 []"><span>3. Bridging the Gap Between Data Science and Production</span></h3>
<p>One of the most significant challenges for organizations is the disconnect between data science and engineering teams. This gap often results in inefficiencies and delayed project launches.</p>
<h4>Operationalizing Data Products</h4>
<p>MIT highlights the growing adoption of data products—solutions that integrate data sources, analytics, and AI into deployable offerings. By leveraging platforms like Zerve, organizations can operationalize data products and streamline the deployment of scalable, production-ready solutions.</p>
<h4>Zerve’s Solutions</h4>
<p>Zerve bridges the gap between data science and production with:</p>
<ul>
<li>Unified Collaboration: Teams can work synchronously or asynchronously on the same codebase transparently, leveraging Zerve’s language interoperability and stable development environment.</li>
<li>Automated Version Control: Real-time tracking ensures every modification is documented and reversible, integrated with Git-based version control systems like GitHub and Bitbucket.</li>
<li>Simplified Handoffs: Deploy with ease through Zerve’s output deployment builder or hand-off a Docker file to engineering, ensuring alignment with CI/CD workflows.</li>
</ul>
<p>By removing bottlenecks and enabling collaboration, Zerve empowers organizations to deploy production-ready solutions faster and more reliably.</p>
<hr>
<h3>The Path Forward: Empowering Organizations in 2025 and Beyond</h3>
<p>Insights from recent events and industry leaders underscore the pressing priorities for organizations:</p>
<ul>
<li>Efficiency: Delivering faster time-to-value with scalable, streamlined workflows.</li>
<li>Governance: Building trust and maintaining compliance with robust controls.</li>
<li>Collaboration: Bridging gaps between data science and production for seamless deployment.</li>
</ul>
<p>Zerve empowers businesses to navigate these challenges with a platform that:</p>
<ul>
<li>Unifies tools and teams for greater collaboration.</li>
<li>Ensures security, observability, and compliance at every stage.</li>
<li>Streamlines deployment workflows for faster, more reliable results.</li>
</ul>
<p>Discover how Zerve can transform your data science and AI development. Visit our website to explore Zerve’s platform, or start your free trial today: <a href="https://www.zerve.ai/resource/free-trial">https://www.zerve.ai/resource/free-trial</a>.&nbsp;</p>
<p>&nbsp;</p>{{ include_custom_fonts({"InterCustom":["Medium","Regular","Semi Bold"],"PPTelegraf":["Bold","Regular"]}) }}

<p>The fields of Data Science and Artificial Intelligence (AI) continue to evolve rapidly, presenting organizations with transformative opportunities and emerging challenges. Insights from recent industry events such as FIMA Europe, the Analytics Institute Summit in Dublin, and Big Data London, combined with findings from the MIT article "<a href="https://sloanreview.mit.edu/article/five-key-trends-in-ai-and-data-science-for-2024/" rel="noopener" target="_blank">Five Key Trends in AI and Data Science for 2024</a>" and <a href="https://www.gartner.com/en/data-analytics/topics/data-governance" rel="noopener" target="_blank">Gartner’s research</a>, highlight the critical priorities shaping the future of data science and AI projects in 2025 and beyond.<br></p>

Zerve AI at Big Data LDN 2024 Wrap Up

Zerve AI at Big Data LDN 2024: Revolutionising Data Science & AI Development

<p><span style="font-family: Arial, Helvetica, sans-serif;">The energy at <a href="https://www.bigdataldn.com/en-gb.html" rel="noopener" target="_blank">Big Data LDN 2024</a> was electric! Data enthusiasts from across the UK (and globally) converged to explore the latest advancements in AI and data science. <a href="https://www.bigdataldn.com/en-gb/exhibitor-list/exhibitor-details.Zerve%2BAI.org-3ec473df-b355-4d1d-a847-4ca977e36a42.html#/" rel="noopener" target="_blank">Zerve </a>was delighted to be a part of this dynamic event, showcasing our <a href="https://www.zerve.ai/" rel="noopener" target="_blank">end-to-end platform</a> designed to simplify and accelerate the journey from data insights to<a href="https://www.zerve.ai/blog/Announcing%20Zerve%20DSDE%2030124" rel="noopener" target="_blank"> real-world impact</a>.</span></p>

<h2><span style="font-family: Arial, Helvetica, sans-serif;">Addressing a Shared Challenge</span></h2>
<p><span style="font-family: Arial, Helvetica, sans-serif;">One of the most prominent themes we encountered was the need to <a href="https://www.zerve.ai/blog/zerve-githubv2-integration" rel="noopener" target="_blank">bridge the gap</a> between data science teams and production environments. Many attendees expressed the challenge of translating data science prototypes into robust production models. This is exactly where Zerve steps in.</span></p>
<h3><br>Zerve: Bridging the Gap</h3>
<p>Our platform empowers data scientists and engineering teams to work with increased transparency and efficiency.&nbsp;</p>
<p>By offering <a href="https://www.zerve.ai/platform-features" rel="noopener" target="_blank">features</a> like:</p>
<ul>
<li>Streamlined workflows for seamless collaboration between data scientists and engineers.</li>
<li>Intuitive user experience that eliminates unnecessary complexity.</li>
<li>Built-in transparency at every stage of development.</li>
<li>Serverless GPU &amp; CPU orchestration for effortless deployment.</li>
<li>Easy API, app, and scheduled job building for production-ready solutions.</li>
<li>Thousands of open-source LLMs are readily available to empower your projects.</li>
</ul>
<p>Zerve empowers businesses to move data science projects from concept to production with confidence. Hear from our Go-To-Market Lead, Zach O'Hagan, as he shares insights from the frontline at Big Data LDN. He spoke with attendees across industries, showcasing how Zerve can bridge the data science and production gap for your business.</p>
<h3>Watch the clip</h3>
<div class="hs-embed-wrapper hs-fullwidth-embed" data-service="youtube" data-responsive="true" style="position: relative; overflow: hidden; width: 100%; height: auto; padding: 0px; min-width: 256px; display: block; margin: auto;"><div class="hs-embed-content-wrapper"><div style="position: relative; overflow: hidden; max-width: 100%; padding-bottom: 56.25%; margin: 0px;"><iframe width="256" height="144.64" src="https://www.youtube.com/embed/QDzQnc8hp5I?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen title="Zerve AI at Big Data London 2024 Wrap Up" style="position: absolute; top: 0px; left: 0px; width: 100%; height: 100%; border: none;"></iframe></div></div></div>
<hr>
<p>{{cta('182498415245','justifycenter')}}Experience the power of Zerve with <a href="https://www.zerve.ai/free-trial" rel="noopener" target="_blank">a 14-day free trial </a>and see firsthand how we bridge the gap between data science and production.</p>
<h3>Looking Ahead</h3>
<p>Zerve is committed to revolutionizing the way businesses approach AI and data science. We're excited to continue the conversation – follow us on<a href="https://www.linkedin.com/company/zerve-ai" rel="noopener" target="_blank"> LinkedIn</a> or subscribe to our <a href="https://www.youtube.com/@ZerveAI" rel="noopener" target="_blank">YouTube</a> channel&nbsp; for the latest updates and exclusive content.</p>{{ include_custom_fonts({"InterCustom":["Medium","Regular","Semi Bold"],"PPTelegraf":["Bold","Regular"]}) }}

<p><span style="font-family: Arial, Helvetica, sans-serif;">The energy at <a href="https://www.bigdataldn.com/en-gb.html" rel="noopener" target="_blank">Big Data LDN 2024</a> was electric! Data enthusiasts from across the UK (and globally) converged to explore the latest advancements in AI and data science. <a href="https://www.bigdataldn.com/en-gb/exhibitor-list/exhibitor-details.Zerve%2BAI.org-3ec473df-b355-4d1d-a847-4ca977e36a42.html#/" rel="noopener" target="_blank">Zerve </a>was delighted to be a part of this dynamic event, showcasing our <a href="https://www.zerve.ai/" rel="noopener" target="_blank">end-to-end platform</a> designed to simplify and accelerate the journey from data insights to<a href="https://www.zerve.ai/blog/Announcing%20Zerve%20DSDE%2030124" rel="noopener" target="_blank"> real-world impact</a>.</span></p>


blog_comments_heading

Phily Hayes

Powerful R Integration: Seamless Interoperability with Python and SQL

Discover Zerve’s powerful R integration for seamless multi-language interoperability. Empower R, Python, and SQL users to collaborate effortlessly within a single workflow.

<p>Are you ready to elevate your data science workflows? Zerve is excited to announce a game-changing update: seamless interoperability between R, Python, and SQL, designed to optimize multi-language environments for data scientists and analysts alike.</p>
<p></p>
<h3>Why R Integration Matters</h3>
<p>For years, the Zerve team has been committed to supporting <strong><a href="https://www.r-project.org/" rel="noopener" target="_blank">R</a> as a first-class citizen</strong> on our platform. Our founding team, deeply rooted in data science, understands the challenges of juggling multiple languages like R, <a href="https://www.python.org/" rel="noopener" target="_blank">Python</a>, and SQL. From <strong>data conversion headaches</strong> to workflow disruptions, we've lived the pain – and we’ve built a solution to transform it into seamless productivity.</p>
<h3>Introducing Seamless Multi-Language Interoperability</h3>
<p><strong>Zerve's powerful R integration</strong> allows you to:</p>
<ul>
<li>Perform <strong>statistical analysis</strong> with R.</li>
<li>Leverage <strong>machine learning</strong> with Python.</li>
<li>Conduct advanced <strong>data manipulation</strong> with SQL.</li>
</ul>
<p>All within a <strong>single, unified workflow</strong>, eliminating the need for complex file conversions or environment switching.</p>
<h3>How It Works: Key Features Driving Integration</h3>
<p>Zerve’s interoperability relies on three cornerstone features:</p>
<ol>
<li>
<p><strong>Universal Output Formats</strong><br>Outputs are saved in common formats like <a href="https://parquet.apache.org/" rel="noopener" target="_blank"><strong>Parquet</strong></a>, ensuring compatibility and efficiency across languages.</p>
</li>
<li>
<p><strong>Metadata Storage</strong><br>Each block stores results with metadata for easy access and clarity, regardless of the language used.</p>
</li>
<li>
<p><strong>Dedicated Runtimes</strong><br>Zerve offers <strong>language-specific runtimes</strong>, guaranteeing smooth data transfer between blocks.</p>
</li>
</ol>
<h3>The Zerve Canvas: Integrated Multi-Language Environment</h3>
<p>Our <strong>Zerve Canvas</strong> empowers data teams to mix and match R, Python, and SQL blocks effortlessly. Pass outputs from one block to another without worrying about serialization or conversion. With Zerve handling the complexities, you’re free to focus on insights and results.</p>
<div class="hs-embed-wrapper hs-fullwidth-embed" data-service="youtube" data-responsive="true" style="position: relative; overflow: hidden; width: 100%; height: auto; padding: 0px; min-width: 256px; display: block; margin: auto;"><div class="hs-embed-content-wrapper"><div style="position: relative; overflow: hidden; max-width: 100%; padding-bottom: 56.25%; margin: 0px;"><iframe width="256" height="144.64" src="https://www.youtube.com/embed/kOHCvNGKA6g?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen title="Interope(R)ability" style="position: absolute; top: 0px; left: 0px; width: 100%; height: 100%; border: none;"></iframe></div></div></div>
<h3 style="font-size: 16px;">&nbsp;</h3>
<h3>Exclusive Features for R Users</h3>
<p>We’re thrilled to offer a suite of <strong>R-specific enhancements</strong> designed to elevate your experience:</p>
<ul>
<li><strong>Native R Runtime:</strong> Built from the ground up in R, featuring parallelization and compute optimization.</li>
<li><strong>RMarkdown Support:</strong> Create dynamic, interactive reports directly in Zerve.</li>
<li><strong>Shiny App Hosting:</strong> Deploy and host <strong>Shiny apps</strong> seamlessly within your Zerve platform.</li>
<li><strong>Intelligent Assistance:</strong> Enjoy tailored code completion and error-handling assistance for R.</li>
</ul>
<h3>Benefits of Zerve’s R Integration for Data Teams</h3>
<ol>
<li><strong>Flexibility</strong>: Use the best tool for each task without being limited by language barriers.</li>
<li><strong>Efficiency</strong>: Save time by eliminating tedious data conversion and switching.</li>
<li><strong>Collaboration</strong>: Bring R, Python, and SQL users together in a single workflow.</li>
<li><strong>Scalability</strong>: Run R code at scale, and hassle-free without DevOps headaches.</li>
</ol>
<h3>Why Choose Zerve for Multi-Language Interoperability?</h3>
<p>Zerve isn’t just a platform; it’s your partner in productivity. Whether you’re an <strong>R enthusiast</strong> or part of a diverse, multi-language team, Zerve empowers you to:</p>
<ul>
<li>Unlock new insights faster.</li>
<li>Scale your workflows efficiently.</li>
<li>Collaborate effortlessly across languages.</li>
</ul>
<h3><strong>{{cta('182501643054','justifycenter')}}</strong></h3>
<h3><strong>Explore Zerve’s Enhanced Multi-Language Capabilities</strong></h3>
<p>Ready to experience the future of data science? Learn more and <a href="https://www.zerve.ai/resource/free-trial" rel="noopener" target="_blank">get started</a> today. We can’t wait to see how you push the boundaries of what’s possible with <strong>R integration</strong> and <strong>multi-language collaboration</strong>.</p>
<p>{{cta('182498415245','justifycenter')}}</p>{{ include_custom_fonts({"InterCustom":["Medium","Regular","Semi Bold"],"PPTelegraf":["Bold","Regular"]}) }}

<p>Are you ready to elevate your data science workflows? Zerve is excited to announce a game-changing update: seamless interoperability between R, Python, and SQL, designed to optimize multi-language environments for data scientists and analysts alike.</p>
<p></p>

Enhance Your Data Science Workflow with Zerve’s GitHubv2 Integration

Zeve's latest innovation: a robust GitHubv2 integration. Closing the gap between the output of data teams and what engineering teams need to operationalize their work is central to Zerve’s mission. With this release, we’re taking a significant leap forward in uniting data science workflows with engineering precision.

<p>Today, we are <span>extremely excited</span> to announce our latest innovation: a robust <a href="https://github.com/Zerve-AI" rel="noopener" target="_blank">GitHubv2 integration</a>. Closing the gap between the output of data teams and what engineering teams need to operationalize their work is central to Zerve’s mission. With this release, we’re taking a significant leap forward in uniting data science workflows with engineering precision.</p>
<p></p>
<h3 data-pm-slice="1 1 []"><span>Building on a Strong Foundation</span></h3>
<p><span>At Zerve, collaboration isn’t just a feature—it’s the backbone of our platform, enabled by our patent-pending architecture. This unique foundation powers stable, collaborative Data Science and AI development like no other tool in the market.</span></p>
<p><span>Our underlying technology ensures consistent and reliable performance, even as projects grow in complexity. This stability has always set Zerve apart, empowering Data and AI teams to push the boundaries of innovation. Now, with enhanced Git integration, we’re delivering even greater capabilities for team-based data science.</span><span></span></p>
<div class="hs-embed-wrapper hs-fullwidth-embed" data-service="youtube" data-responsive="true" style="position: relative; overflow: hidden; width: 100%; height: auto; padding: 0px; min-width: 256px; display: block; margin: auto;"><div class="hs-embed-content-wrapper"><div style="position: relative; overflow: hidden; max-width: 100%; padding-bottom: 56.25%; margin: 0px;"><iframe width="256" height="144.64" src="https://www.youtube.com/embed/vh2COmqkIeI?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen title="Zerve &amp; GitHub" style="position: absolute; top: 0px; left: 0px; width: 100%; height: 100%; border: none;"></iframe></div></div></div>
<p>&nbsp;</p>
<h3 data-pm-slice="1 1 []"><span>Introducing GitHubv2 Integration: Revolutionizing Data Collaboration</span></h3>
<p>The new GitHubv2 integration, accessible via a <a href="https://github.com/Zerve-AI" rel="noopener" target="_blank">GitHub app</a>, provides seamless connectivity between Zerve projects and GitHub repositories. Here’s what you can do:</p>
<ul>
<li>Connect directly to GitHub repositories: Link your Zerve projects for unified data and code management.</li>
<li>Perform source control operations within Zerve: Commit, push, pull, and manage branches right in your canvas.</li>
<li>Effortless branch management: Simplify your workflows with intuitive tools for creating and switching branches.</li>
</ul>
<p>This integration bridges the gap between data science workflows and the best practices of software engineering, ensuring your team operates with the structure and rigour of top-tier engineering environments.</p>
<h3>Beyond Version Control: Ecosystem Synergy</h3>
<p><a href="https://github.com/Zerve-AI" rel="noopener" target="_blank">GitHubv2</a> isn’t just about version control. By placing Git at the core of Zerve’s platform, we’ve unlocked a host of additional features:</p>
<ul>
<li>Seamless integration with issue tracking tools like Linear or Jira</li>
<li>Project planning and step mapping directly in Zerve</li>
<li>Automatic syncing of branch names across tools</li>
<li>Real-time reflection of pull requests and merges in your issue tracker</li>
</ul>
<p>This interconnected ecosystem empowers managers and team leads to effortlessly track progress and ensure alignment across teams, fostering smooth collaboration at every stage.<br><br></p>
<h3>Aligning Data Science with Engineering Workflows</h3>
<p>Modern development demands alignment with CI/CD processes. With <a href="https://github.com/Zerve-AI" rel="noopener" target="_blank">GitHubv2</a>, Zerve enables data teams to:<br><br></p>
<ul>
<li>Initiate CI/CD workflows: Automatically kick off CI/CD pipelines aligned with engineering standards.</li>
<li>Maintain branch flexibility: Tailor branch management to suit your organization’s specific needs.</li>
<li>Seamlessly transition between versions: Minimize friction when updating or deploying new project iterations.</li>
</ul>
<p>{{cta('182501643054','justifycenter')}}</p>
<h3>Take Your Data Science Collaboration to the Next Level</h3>
<p>Ready to elevate your data team’s collaboration, version control, and project management capabilities? <a href="https://github.com/Zerve-AI" rel="noopener" target="_blank">Zerve’s GitHubv2 </a>integration delivers the tools you need to bridge the gap between data science and engineering.</p>
<p><a href="https://www.zerve.ai/resource/free-trial" rel="noopener" target="_blank">Try it now </a>and get started with GitHubv2 today. Experience the future of collaborative data science with Zerve!</p>
<p>{{cta('182498415245','justifycenter')}}</p>{{ include_custom_fonts({"InterCustom":["Medium","Regular","Semi Bold"],"PPTelegraf":["Bold","Regular"]}) }}

<p>Today, we are <span>extremely excited</span> to announce our latest innovation: a robust <a href="https://github.com/Zerve-AI" rel="noopener" target="_blank">GitHubv2 integration</a>. Closing the gap between the output of data teams and what engineering teams need to operationalize their work is central to Zerve’s mission. With this release, we’re taking a significant leap forward in uniting data science workflows with engineering precision.</p>
<p></p>

Zerve Raises $7.6M to Maximise AI & Data Business Impact

Zerve, the data science and AI development platform that empowers code-first data teams to cut cycle times by up to 9x, announced today that it has raised a seed round of $7.6M, led by Paladin Capital Group, with participation from existing investor Elkstone and angels including former Executive Vice President of Engineering at DataRobot, Rob Hickey. 


<p style="text-align: left;">We're thrilled to announce that Zerve has closed a $7.6 million seed funding round, led by <a href="http://www.paladincapgroup.com" rel="noopener" target="_blank">Paladin Capital Group</a>, with participation from existing investor <a href="https://www.elkstonepartners.com/" rel="noopener" target="_blank">Elkstone</a> and angels including former Executive Vice President of Engineering at DataRobot, Rob Hickey. This significant milestone marks a major step forward in our mission to elevate the impact of data teams with our innovative data science and AI development platform.</p>

<p style="text-align: left;">Established in 2021, Zerve is on a mission to elevate the impact of AI and data workflows by providing a unique, developer-centric platform based on its filed-patent architecture that guarantees resilience and scalability.</p>
<p style="text-align: left;">Today, the efficiency and measurable impact of code-first data teams are becoming more scrutinized within enterprises. Many AI and data workflows that could have transformative effects on organizations do not leave the development stage. Those that do have to overcome many hurdles including deployment handovers to separate development and engineering teams, and issues with version control, reproducibility, and cloud infrastructure.&nbsp;<br><br>Zerve’s end-to-end platform provides a robust environment that simplifies the building, managing and deployment of these workflows. By automating away the challenges and complexities that face code-first data teams, Zerve enables them to significantly reduce cycle times and maximise ROI. With features like automated infrastructure management, language interoperability, robust version controls, package management and deployment processes, Zerve is uniquely positioned to power the next wave of high-performing data teams to maximise their impact and reach their true potential.</p>
<p style="text-align: left;">Zerve raised $3.8m in <a href="https://www.businesspost.ie/news/irish-ai-firm-zerve-raises-3-8m-in-pre-seed-funding-round/" rel="noopener" target="_blank">pre-seed funding</a> in October 2023 led by Elkstone, and commercially launched its product in February of this year. It has successfully onboarded elite code-first data teams in major organisations globally to help them cut cycle times by up to 9x, with over 4,000 users signing up to its free product tier in just three months. The startup is led by three co-founders: <a href="https://www.linkedin.com/in/phily-hayes-saas/" rel="noopener" target="_blank">Phily Hayes</a> (CEO &amp; Co-founder), formerly at LearnUpon; <a href="https://www.linkedin.com/in/jason-hillary/" rel="noopener" target="_blank">Jason Hilary</a> (CTO &amp; Co-founder), who brings AI/ML experience from Siren and Antares Vision; and <a href="https://www.linkedin.com/in/greg-michaelson/" rel="noopener" target="_blank">Dr Greg Michaelson </a>(CPO &amp; Co-founder) – previously Chief Customer Officer at DataRobot.<br><br>Phily said: <span style="color: #cfe2f3;">“<span style="text-decoration: none;">The stick we measure ourselves by is how much more throughput the teams we’re working with have been able to achieve, whether that is reducing their overall cycle time for projects or increasing their speed to recommendations. The speed at which data and AI projects run can sometimes feel like death by 1,000 cuts, so on-top of our 100% guaranteed, resilience-based architecture, we have cleared the path-to-output by removing the smaller hurdles that get in the way.</span>”&nbsp;</span></p>
<p style="text-align: left;"><a href="https://www.linkedin.com/in/kenneth-pentimonti-246a342b/" rel="noopener" target="_blank">Ken Pentimonti</a>, Managing Director, Paladin Capital Group added: <span style="color: #cfe2f3;">“The limitations of current solutions have placed a big barrier in front of innovation at organizations seeking to realise the potential of data science and AI. Zerve’s intuitive platform can release that potential and improve the safety and reliability of the software ultimately deployed. With spend on AI at $33Bn in 2023 in Europe alone, growing 30% per year, we are hugely excited about Zerve’s prospects.”</span></p>
<p style="text-align: left;">With this strategic funding, Zerve is poised to accelerate its research and development efforts and expand its reach to empower even more high-performing data teams. We're excited about the future of code-first data science, and we're committed to providing the tools and platform to revolutionize how AI is developed and deployed.&nbsp;Zerve was advised by <a href="https://www.capnua.com/" rel="noopener" target="_blank">Capnua Corporate Finance</a> and <a href="https://www.rdj.ie/" rel="noopener" target="_blank">RDJ</a> on the transaction.</p>{{ include_custom_fonts({"InterCustom":["Medium","Regular","Semi Bold"]}) }}

<p style="text-align: left;">We're thrilled to announce that Zerve has closed a $7.6 million seed funding round, led by <a href="http://www.paladincapgroup.com" rel="noopener" target="_blank">Paladin Capital Group</a>, with participation from existing investor <a href="https://www.elkstonepartners.com/" rel="noopener" target="_blank">Elkstone</a> and angels including former Executive Vice President of Engineering at DataRobot, Rob Hickey. This significant milestone marks a major step forward in our mission to elevate the impact of data teams with our innovative data science and AI development platform.</p>


Zach O'Hagan

Zerve Exhibiting at Big Data London 2024

Zerve will be exhibiting at Big Data LDN (London) 2024, the UK's leading event for data, analytics, and AI. Join us on the 18th and 19th of September at stand X571 to explore the future of AI and data science.

<p>We are excited to announce that<strong><span>&nbsp;</span><a href="https://www.zerve.ai/">Zerve</a></strong><span>&nbsp;</span>will be exhibiting at<span>&nbsp;</span><a href="https://www.bigdataldn.com/"><strong>Big Data LDN (London) 2024</strong></a>, the UK's leading event for data, analytics, and AI. Join us on the 18th and 19th of September at<span>&nbsp;</span><a href="https://www.bigdataldn.com/en-gb/exhibitor-list/exhibitor-details.zerve%20ai.org-3ec473df-b355-4d1d-a847-4ca977e36a42.html#/"><strong>stand X571</strong></a><span>&nbsp;</span>to explore the future of AI and data science. Big Data LDN is the ultimate gathering for professionals eager to learn the latest innovations and strategies in data, AI, and analytics. Zerve is honoured to join industry leaders from some of the world’s top brands in showcasing how data science and AI can reshape business outcomes. And we can’t wait to show you how we are redefining the possibilities of AI innovation.</p>
<div>
<h3 id="2486daf65e9f"><strong>Zerve: Simplifying AI &amp; Data Science with Transparency &amp; Efficiency</strong></h3>
</div>
<p>At Zerve, we’ve built an<strong><span>&nbsp;</span><a href="https://www.zerve.ai/platform-features">end-to-end data science and AI development platform</a></strong><span>&nbsp;</span>designed to help<strong><span>&nbsp;</span><a href="https://www.zerve.ai/platform-features">code-first data teams</a></strong><span>&nbsp;</span>operate more transparently, and significantly reduce their prototype to production time. We help businesses and data scientists transform the complex into the achievable, enabling efficient collaboration between data teams and engineers.</p>
<p>Our platform empowers users by:</p>
<ul>
<li><a href="https://www.zerve.ai/why-zerve?__hstc=159041573.3216dc9b1de3ff7dd058f91fd6b505d9.1733139729732.1733139729732.1734427412508.2&amp;__hssc=159041573.1.1734427412508&amp;__hsfp=2586932413"><strong>Closing the gap between data teams and engineers</strong></a>, ensuring streamlined workflows.</li>
<li>Delivering an<strong><span>&nbsp;</span><a href="https://www.zerve.ai/why-zerve?__hstc=159041573.3216dc9b1de3ff7dd058f91fd6b505d9.1733139729732.1733139729732.1734427412508.2&amp;__hssc=159041573.1.1734427412508&amp;__hsfp=2586932413">intuitive, user-friendly experience</a></strong><span>&nbsp;</span>that removes unnecessary complications.</li>
<li>Enabling teams to swiftly transition projects from prototype to production, with<span>&nbsp;</span><a href="https://www.zerve.ai/platform-features?__hstc=159041573.3216dc9b1de3ff7dd058f91fd6b505d9.1733139729732.1733139729732.1734427412508.2&amp;__hssc=159041573.1.1734427412508&amp;__hsfp=2586932413"><strong>built-in transparency</strong></a><span>&nbsp;</span>at every step.</li>
<li>Supporting<span>&nbsp;</span><a href="https://www.zerve.ai/platform-features?__hstc=159041573.3216dc9b1de3ff7dd058f91fd6b505d9.1733139729732.1733139729732.1734427412508.2&amp;__hssc=159041573.1.1734427412508&amp;__hsfp=2586932413"><strong>serverless GPU &amp; CPU orchestration</strong>,<span>&nbsp;</span><strong>IaC</strong>,<span>&nbsp;</span><strong>containerisation</strong>,<span>&nbsp;</span><strong>logs</strong>, and<span>&nbsp;</span><strong>domain management</strong><span>&nbsp;</span></a>to ease operational tasks.</li>
<li>Providing the tools to easily build and<span>&nbsp;</span><a href="https://www.zerve.ai/platform-features?__hstc=159041573.3216dc9b1de3ff7dd058f91fd6b505d9.1733139729732.1733139729732.1734427412508.2&amp;__hssc=159041573.1.1734427412508&amp;__hsfp=2586932413"><strong>deploy APIs, apps, and scheduled jobs</strong></a><span>&nbsp;</span>that are ready for production.</li>
<li>Providing thousands of<span>&nbsp;</span><a href="https://www.zerve.ai/ide-for-llms?__hstc=159041573.3216dc9b1de3ff7dd058f91fd6b505d9.1733139729732.1733139729732.1734427412508.2&amp;__hssc=159041573.1.1734427412508&amp;__hsfp=2586932413"><strong>open-source LLMs</strong></a>, immediately accessible for your projects.</li>
</ul>
<p>With Zerve, businesses can confidently move their data science projects from concept to production, knowing that our platform is designed to deliver clear, transparent, and scalable solutions.</p>
<div>
<h3 id="cb5072a57043"><strong>Meet the Zerve Team</strong></h3>
</div>
<p>Our team will be present at Big Data LDN 2024 to guide you through our platform and demonstrate how Zerve can be tailored to your organisation’s specific needs. You’ll have the opportunity to meet:</p>
<ul>
<li><a href="https://www.linkedin.com/in/phily-hayes-saas/"><strong>Phily Hayes</strong></a>, CEO &amp; Co-Founder</li>
<li><a href="https://www.linkedin.com/in/zach-o-hagan-934187118/"><strong>Zach O'Hagan</strong></a>, Go-To-Market Lead</li>
<li><a href="https://www.linkedin.com/in/jamie-chen-hou/"><strong>Jamie Hou</strong></a>, Head of Marketing</li>
</ul>
<p>&nbsp;</p>
<div>
<h3 id="dfd9d2803551"><strong>Experience Transparent, End-to-End AI &amp; Data Science Solutions</strong></h3>
</div>
<p>Zerve is changing the way businesses approach data science and AI development by providing a transparent, efficient path to production. Join us at<span>&nbsp;</span><a href="https://www.bigdataldn.com/en-gb/exhibitor-list/exhibitor-details.zerve%20ai.org-3ec473df-b355-4d1d-a847-4ca977e36a42.html#/"><strong>stand X571</strong></a><span>&nbsp;</span>at Big Data LDN 2024 and discover how Zerve can simplify your AI journey and drive business value.</p>
<div>
<h3 id="49167df479bc"><strong>Request Your<span>&nbsp;</span><a href="https://hubs.ly/Q02Pjp8p0">30-Day Free Trial</a></strong></h3>
</div>
<p>We’re offering a free 30-day trial to experience Zerve’s end-to-end platform first-hand for the attendees of Big Data LDN 2024. Fill out<span>&nbsp;</span><strong><a href="https://hubs.ly/Q02Pjp8p0">our form</a><span>&nbsp;</span></strong>and our team will guide you through the setup process.</p>
<div>
<h3 id="4b0d60f38d15"><strong>Stay Connected</strong></h3>
</div>
<p>For the latest updates on Zerve and to learn more about our platform, follow us on<strong><span>&nbsp;</span><a href="https://www.linkedin.com/company/zerve-ai/">LinkedIn</a><span>&nbsp;</span></strong>and<strong><span>&nbsp;</span><a href="https://www.youtube.com/@ZerveAI-hk1dh">YouTube</a></strong>. We’ll be sharing exclusive content leading up to the event, so don’t miss out!t</p>{{ include_custom_fonts({"InterCustom":["Medium","Regular","Semi Bold"],"PPTelegraf":["Bold","Regular"]}) }}

<p>We are excited to announce that<strong><span>&nbsp;</span><a href="https://www.zerve.ai/">Zerve</a></strong><span>&nbsp;</span>will be exhibiting at<span>&nbsp;</span><a href="https://www.bigdataldn.com/"><strong>Big Data LDN (London) 2024</strong></a>, the UK's leading event for data, analytics, and AI. Join us on the 18th and 19th of September at<span>&nbsp;</span><a href="https://www.bigdataldn.com/en-gb/exhibitor-list/exhibitor-details.zerve%20ai.org-3ec473df-b355-4d1d-a847-4ca977e36a42.html#/"><strong>stand X571</strong></a><span>&nbsp;</span>to explore the future of AI and data science. Big Data LDN is the ultimate gathering for professionals eager to learn the latest innovations and strategies in data, AI, and analytics. Zerve is honoured to join industry leaders from some of the world’s top brands in showcasing how data science and AI can reshape business outcomes. And we can’t wait to show you how we are redefining the possibilities of AI innovation.</p>


Software Engineering Students Shine at Zerve Hackathon

A recap of the winning submission in Zerve's recent Hackathon, where some very talented students from the University of Limerick’s Immersive Software Engineering program took part 

<div>
<h3 id="49e1c9cde2ff">University of Limerick’s Immersive Software Engineering Students Shine at Zerve Hackathon</h3>
</div>
<p>A couple of weeks ago, a group of incredibly talented students from the University of Limerick’s Immersive Software Engineering program showed off their innovative skills at the Zerve Hackathon. The course was divided into teams and each team demonstrated exceptional creativity and technical skills, with one team ultimately taking home the top prize.</p>
<div>
<h3 id="3b206b35cd08">The Challenge</h3>
</div>
<p>The hackathon, hosted by Zerve, posed a unique challenge to participants: using Zerve, participants applied a Large Language Model (LLM) of their choice to address a real or hypothetical use case. Points were awarded for enhancing the performance of the chosen model either by using Retrieval-Augmented Generation (RAG) or by fine-tuning the model using new data. Teams had to develop a prototype or script to demonstrate their solution and show that their model enhancements improved the performance of the LLM alone.</p>
<div><img src="https://20289263.fs1.hubspotusercontent-na1.net/hubfs/20289263/1-1.png" width="1200" height="800" loading="lazy" alt="1-1" style="height: auto; max-width: 100%; width: 1200px;"></div>
<div>&nbsp;</div>
<div>
<h3 id="db49b6232117">The Winning Solution: Dionysus - The God of Entertainment</h3>
</div>
<p>The winning team from the University of Limerick impressed judges with their project, Dionysus - The God of Entertainment. This innovative solution aimed to perform feature extraction to find the key characteristics of a piece of media, and find the types of audience who would most resonate with this media.</p>
<div>
<h3 id="8dc3e6ea9c9d">1. Initial Approach with Claude3:</h3>
</div>
<ul>
<li>The team started by using Claude3 alone to generate recommendations based on user input. For example, they asked for recommendations for people who like "Dune."</li>
<li>The results were uninteresting and repetitive, as they mostly suggested "Dune" itself, highlighting the need for a different approach.</li>
</ul>
<div><img src="https://20289263.fs1.hubspotusercontent-na1.net/hubfs/20289263/2-1.png" width="702" height="468" loading="lazy" alt="2-1" style="height: auto; max-width: 100%; width: 702px;"></div>
<div>&nbsp;</div>
<div>
<h3 id="82494268920e">2. Implementing RAG:</h3>
</div>
<ul>
<li>To enhance their solution, the team decided to use RAG (Retrieval-Augmented Generation).</li>
<li>They incorporated several APIs to fetch trending data, including a trending movies API, trending TV shows API, NYT Best Seller API, and Spotify podcast API.</li>
<li>This external data was used to enrich the LLM prompts, ensuring more diverse and relevant recommendations.</li>
</ul>
<div><img src="https://20289263.fs1.hubspotusercontent-na1.net/hubfs/20289263/3-1.png" width="1152" height="768" loading="lazy" alt="3-1" style="height: auto; max-width: 100%; width: 1152px;"></div>
<div>&nbsp;</div>
<div>
<h3 id="5ddefe07124b">3. Generating Enhanced Recommendations:</h3>
</div>
<ul>
<li>With the additional data from the APIs, the team created prompts that included genre and other characteristics of the media.</li>
<li>For instance, they generated prompts asking for five recommended pieces of media similar to "Dune," based on specific features.</li>
<li>This approach yielded more interesting and relevant results, providing diverse recommendations related to "Dune."</li>
</ul>
<p><img src="https://20289263.fs1.hubspotusercontent-na1.net/hubfs/20289263/4.png" width="1200" height="800" loading="lazy" alt="4" style="height: auto; max-width: 100%; width: 1200px;"></p>
<div>
<h3 id="fc5c83505b99">4. Providing Explanations for Recommendations:</h3>
</div>
<ul>
<li>To enhance the user experience further, the team generated explanations for why particular recommendations were made.</li>
<li>They chose a model from Microsoft and fine-tuned it using additional data, leveraging GPUs provided by Zerve.</li>
<li>This refinement improved the model's ability to produce accurate and insightful explanations, enhancing the overall recommendation quality.</li>
</ul>
<div><img src="https://20289263.fs1.hubspotusercontent-na1.net/hubfs/20289263/5.png" width="807" height="538" loading="lazy" alt="5" style="height: auto; max-width: 100%; width: 807px;"></div>
<div>&nbsp;</div>
<div>
<h3 id="9987a6791f11">5. Ensuring Data Security with Zerve:</h3>
</div>
<ul>
<li>One advantage of using Zerve for this project was the ability to host the fine-tuned model within Zerve.</li>
<li>This ensured that user prompts and data were not sent to third parties, protecting sensitive information and maintaining data security.</li>
</ul>
<div>
<h3 id="2093aab98220">6. Creating Visual Enhancements:</h3>
</div>
<ul>
<li>The final step involved creating a visual representation of the recommendations.</li>
<li>The team used Claude 3 to generate prompts for an image generation AI, asking it to create images for the recommended media.</li>
<li>These images were then produced by another LLM, adding a visual element that complemented the recommendations and rounded off the project nicely.</li>
</ul>
<div><img src="https://20289263.fs1.hubspotusercontent-na1.net/hubfs/20289263/6.png" width="735" height="490" loading="lazy" alt="6" style="height: auto; max-width: 100%; width: 735px;"></div>
<div>&nbsp;</div>
<div>
<h3 id="000dea835b64">Enhancing LLM Performance</h3>
</div>
<p>The team’s solution addressed several fundamental flaws commonly found in traditional LLMs used for media recommendations:</p>
<ul>
<li><strong>Lack of New Data</strong>: Most LLMs only have data up until 2021/2022. By performing API calls to fetch current trending media, the solution ensured that recommendations were relevant and up-to-date.</li>
<li><strong>Tunnel Vision</strong>: Traditional LLMs often focus too narrowly on closely related media. Dionysus generates recommendations based on the features and themes of the media rather than just the title, providing more diverse and nuanced options.</li>
<li><strong>Hallucinations</strong>: LLMs sometimes produce incorrect or fictitious information. The solution grounded the LLM in reality by providing accurate data and employing specific prompts to reduce hallucinations.</li>
<li><strong>Discrepancies in Outputs</strong>: The formatting of outputs can be inconsistent. Dionysus provided clear instructions to the LLMs, ensuring reliable and consistent output formats.</li>
<li><strong>One Task per Prompt</strong>: Traditional LLMs can typically handle only one task at a time. The solution utilized parallel processes to generate images and text simultaneously, creating a more interactive and enriched user experience.</li>
</ul>
<div><img src="https://20289263.fs1.hubspotusercontent-na1.net/hubfs/20289263/7.png" width="1200" height="800" loading="lazy" alt="7" style="height: auto; max-width: 100%; width: 1200px;"></div>
<div>&nbsp;</div>
<div>
<h3 id="58e5ed312b1f">Conclusion</h3>
</div>
<p>We were genuinely impressed by the quality of work and innovation demonstrated by all the teams that took part in the Zerve Hackathon. The winning project, Dionysus, not only showcased advanced technical skills but also a deep understanding of how to enhance large language models with real-time data and API integrations. The thoughtful approach and execution reflected their ability to tackle complex problems creatively and effectively. You can check out their winning submission for yourself<span>&nbsp;</span><a href="https://app.zerve.ai/canvas/df73f97c-062e-4ccc-9c09-3d9370518105">right here.</a></p>
<p>We're excited to see what the future holds for these students, as their impressive performance at the hackathon suggests they have much to contribute to the field of AI.</p>{{ include_custom_fonts({"InterCustom":["Medium","Regular","Semi Bold"],"PPTelegraf":["Bold","Regular"]}) }}

<div>
<h3 id="49e1c9cde2ff">University of Limerick’s Immersive Software Engineering Students Shine at Zerve Hackathon</h3>
</div>


Jason Hillary

Llama 3 for a personalized tour guide in Italy 

Fine-Tuning Llama 3 in Zerve

Fine-tuning Llama 3 for a personalized tour guide in Italy 

<div>
<h3 id="88fccb3c9fdf">Fine -Tuning Llama 3 in Zerve</h3>
</div>

<div>
<h3 id="a0c6c759454d">Introduction</h3>
</div>
<p>In late April, Meta released Llama 3 and it quickly garnered attention for its enhanced capabilities in language understanding and generation. The model boasts substantial improvements, including a context length of up to 8,192 tokens, compared to 4,096 in Llama 2, but it also introduces a new tokenizer with a 128K-token vocabulary.</p>
<p>We thought it would be fun to leverage our Hugging Face integration in Zerve to import Llama3 into a canvas and fine-tune it to build a personalized travel itinerary for a trip to Italy. Because Zerve persists your models, variables and data in the canvas, once I import Llama3, or any other LLM, this becomes a fully hosted, accessible, private version of that model.</p>
<div>
<h3 id="4d0772813834"></h3>
</div>
<div>
<h3 id="c7b1ac666f7c">Understanding the Fine-Tuning Technique Used</h3>
</div>
<p>In the journey of adapting Llama 3 to generate personalized travel itineraries, the fine-tuning approach employed here utilizes the model's pre-trained capabilities augmented by custom input prompts. This technique is less about traditional fine-tuning—where the model's weights are extensively retrained—and more about leveraging the model's existing knowledge base, applying specific prompts to guide the generation process.</p>
<p>This method can be seen as "light" fine-tuning or prompt engineering, where the goal is to tailor the AI's responses to detailed user queries through smart input manipulation. It is particularly effective when the pre-trained model already possesses a substantial understanding of the content domain, allowing for refined output tailored to specific requirements without the need for deep, computational retraining.</p>
<p>&nbsp;</p>
<div>
<h3 id="72c3321d1efd">Technical Deep Dive: Fine-Tuning Llama 3 on Zerve</h3>
</div>
<p>In this section, we explore the technical details of fine-tuning Llama 3 using a custom-built scenario as a tour guide in Italy. The code snippet below shows how I utilized Zerve's capabilities to adapt Llama 3 for generating personalized travel itineraries.</p>
<p>&nbsp;</p>
<div>
<h3 id="6dd069c89aec">Code Initialization and Setup</h3>
</div>
<p>Firstly, I initialized the tokenizer and model using the AutoTokenizer and AutoModelForCausalLM classes, setting up the model with optimized configurations for the task.</p>
<div><img src="https://20289263.fs1.hubspotusercontent-na1.net/hubfs/20289263/1-3.png" width="648" height="432" loading="lazy" alt="1-3" style="height: auto; max-width: 100%; width: 648px;"></div>
<div>&nbsp;</div>
<div>
<h3 id="3847317c961b">Data Preparation</h3>
</div>
<p>I then prepared the input by setting the context and a complex user query for a three-week tour in Italy. This setup illustrates how to personalize responses based on user input.</p>
<div><img src="https://20289263.fs1.hubspotusercontent-na1.net/hubfs/20289263/2-2.png" width="531" height="354" loading="lazy" alt="2-2" style="height: auto; max-width: 100%; width: 531px;"></div>
<div>&nbsp;</div>
<div>
<h3 id="6e02881b27a8">Generating Response</h3>
</div>
<p>I used the model's generate function to produce a detailed itinerary, showcasing the model's ability to handle nuanced tasks.</p>
<div><img src="https://20289263.fs1.hubspotusercontent-na1.net/hubfs/20289263/3-2.png" width="1182" height="788" loading="lazy" alt="3-2" style="height: auto; max-width: 100%; width: 1182px;"></div>
<div>&nbsp;</div>
<div>
<h3 id="ca2e0c2b8066">GPU Infrastructure</h3>
</div>
<p>Using <a href="https://www.zerve.ai/ide-for-llms" rel="noopener" target="_blank">Zerve’s IDE</a>, I was able to run serverless GPUs without having to worry about managing the complex infrastructure. At code block level, I selected the relevant GPU I wanted to use, and also had the option for CPUs and lambdas for other code blocks if necessary.</p>
<p><img src="https://20289263.fs1.hubspotusercontent-na1.net/hubfs/20289263/4-1.png" width="1200" height="800" loading="lazy" alt="4-1" style="height: auto; max-width: 100%; width: 1200px;"></p>
<div>
<h3 id="bd4890e71192">Results</h3>
</div>
<p>The results generated a tour itinerary for a family of three travelling across Italy, with locations to visit, daily plans as well as recommendations on where to stay and what to eat.</p>
<div><img src="https://20289263.fs1.hubspotusercontent-na1.net/hubfs/20289263/5-1.png" width="1200" height="800" loading="lazy" alt="5-1" style="height: auto; max-width: 100%; width: 1200px;"></div>
<p>&nbsp;</p>
<div>
<h3 id="3a6c9bdbb762">Efficiency and Privacy</h3>
</div>
<p>While my travel plans on a trip to Italy would not exactly be classified as sensitive data, because Zerve is self-hosted through <a href="https://aws.amazon.com/marketplace/seller-profile?id=seller-bfwm5mq4awafy" rel="noopener" target="_blank">AWS</a>, had I wanted to expose Llama 3 to any sensitive data, I could do so with ease from the safety of my own, private cloud infrastructure. This addresses a major privacy concern of many businesses around sensitive data leaking to third party prompts when fine-tuning these models in other environments.</p>
<p>&nbsp;</p>
<div>
<h3 id="acd62e7b925e">Conclusion</h3>
</div>
<p>Diving into fine-tuning Llama 3 using Zerve turned out to be an intriguing venture. It was cool to see how we could nudge a high-powered model like Llama 3 to generate personalized travel plans with just a bit of clever prompt engineering—no heavy lifting required in terms of retraining. Using Zerve's serverless GPUs, the technical process was streamlined, allowing me to focus more on the creative aspects of AI applications rather than getting bogged down by infrastructure management.</p>
<p>The final itinerary for a family trip across Italy was both detailed and thoughtful, suggesting neat spots to visit, cozy places to stay, and delicious local eats. Honestly, while the itinerary sounded like a dream vacation, I'd personally dial down the travel a bit for some extra relaxation time.</p>
<p>And with everything hosted securely on our own cloud through AWS, privacy concerns were the last thing on my mind, even when working with non-sensitive data. This project was a neat demonstration of how tailor-made AI applications can really shine, blending efficiency with a keen respect for user privacy.</p>
<p>&nbsp;</p>
<div>
<h3 id="5d24fa4ae7ba">Call to Action</h3>
</div>
<p>You can check out the canvas and code for yourself by following<span>&nbsp;</span><a href="https://app.zerve.ai/canvas/bdea54b4-9335-455c-aa75-8955f5dbadfd">this link.</a><span>&nbsp;</span>Feel free to comment or ask questions using comments in the canvas, we’d love to hear from you!</p>{{ include_custom_fonts({"InterCustom":["Medium","Regular","Semi Bold"],"PPTelegraf":["Bold","Regular"]}) }}

<div>
<h3 id="88fccb3c9fdf">Fine -Tuning Llama 3 in Zerve</h3>
</div>


Amongst Great Compan(ies)

<p>Our recent participation in the Intel Ignite Program in Munich marked a pivotal moment in our company's trajectory. What began as an opportunity to connect with fellow European Deeptech companies evolved into a transformative experience that reshaped our approach to business in unexpected ways.</p>
<p>From the outset, including the extensive selection process, the program radiated with high-energy, attracting us in due to the level of innovation we could see which was present - This was something I would not have previously associated Intel with -<span>&nbsp;</span><strong>High Energy Environments, Startups &amp; Innovation</strong>. Although I can't comment about Intel at large, Intel Ignite is a place that has these in abundance.</p>
<div>
<h3 id="dcfdd63d1cbf">OKRs &amp; E.O.S.</h3>
</div>
<p>One of the most remarkable outcomes of our Intel Ignite journey was the evolution of our day-to-day operations. Initially we had a set of goals and outcomes we wanted out of the program, but it was actually the structure of this goal &amp; objective setting system that became the biggest value creation for us. When it comes to mastering the art of Objectives and Key Results (OKRs), who better to learn from than the pioneers themselves? Intel. Maybe this should not have come as a surprise but with Intel's insights into the power of OKRs in driving organizational alignment and performance, along with daily/weekly/monthly alignment checks, we really changed how we operate.</p>
<p>Implementing OKRs and adopting the Level 10 Meeting framework from the<span>&nbsp;</span><a href="https://www.eosworldwide.com/what-the-heck-is-eos">Entrepreneurial Operating System (EOS)</a><span>&nbsp;</span>were transformative steps that brought clarity, alignment, and accountability to our team. Personally, I know leaving our weekly Level 10 Meeting, exactly what I should and should not be worried about / focused on.</p>
<p>The combination of OKRs and Level 10 Meetings worked seamlessly for us, allowing us to set ambitious objectives while ensuring that everyone in the organization was aligned and accountable for their contributions. The Level 10 Meetings, with their focus on problem-solving and accountability, provided the perfect platform for us to track our progress towards our OKRs and address any obstacles in real-time. We have even implemented a second Level 10 meeting associated with our Engineering team. The key for me is that the level 10 meeting happens every week, with the same people, the same agenda, and at the same time. Consistency is what makes it work.</p>
<div>
<h3 id="ac1b2be78de3">The Cohort</h3>
</div>
<p>However, beyond the strategic frameworks and business insights gained, the other true value of our Intel Ignite experience lay in the connections forged within the cohort. The camaraderie and support shared among our peers was awesome. Through discussions, problem-solving, and encouragement, we discovered the power of community in propelling us towards our goals. It was super insightful to see the common struggles amongst the teams as they tried to build their solutions. It was within this environment that we figured out how far we had truly come, and to take on the mindset of,<span>&nbsp;</span><em>"of course there will be issues to solve each day, we are working on a monumental problem!</em>". This mindset was the motivation to push the boundaries of what we thought possible.</p>
<p>Special shout out to all the companies involved:</p>
<ol>
<li><a href="https://www.proximafusion.com/">Proxima Fusion</a><span>&nbsp;</span>– Pioneering the construction of stellarator fusion plants</li>
<li><a href="https://www.giskard.ai/">Giskard</a><span>&nbsp;</span>– Advanced testing and QA framework tailored for AI systems</li>
<li><a href="https://www.quantumdiamonds.de/">Quantum Diamonds</a><span>&nbsp;</span>– Quantum sensing revolutionizing semiconductor technology</li>
<li><a href="https://www.dotphoton.com/">Dotphoton</a><span>&nbsp;</span>– Industry-leading data processing and compression at massive scales</li>
<li><a href="https://www.fonontech.com/">FononTech</a><span>&nbsp;</span>– Innovative impulse printing of 3D interconnects for vertical chips</li>
<li><a href="https://www.semron.ai/">Semron</a><span>&nbsp;</span>– Cutting-edge high-density edge AI inference chip technology</li>
<li><a href="https://www.deepdetection.tech/">Deep Detection</a><span>&nbsp;</span>– High-speed, multispectral analysis technology for comprehensive detection</li>
<li><a href="https://www.superduperdb.com/">SuperDuper</a><span>&nbsp;</span>– Integrating AI seamlessly into database operations</li>
<li><a href="https://electric-sql.com/">ElectricSQL</a><span>&nbsp;</span>- The conflict free sync layer for local apps.</li>
</ol>
<p>Finally, the mentors in the program were fantastic, and I know they will continue to guide Zerve in what we build from here on out, to name the core team: Markus Bohl, Martha Ivanovas, Alois Eder &amp; Kate Hach. Rockstar support structure that we are truly grateful for. If any Deeptech founder is considering the program, feel free to dm me for my thoughts.</p>{{ include_custom_fonts({"InterCustom":["Medium","Regular","Semi Bold"],"PPTelegraf":["Bold","Regular"]}) }}

<p>Our recent participation in the Intel Ignite Program in Munich marked a pivotal moment in our company's trajectory. What began as an opportunity to connect with fellow European Deeptech companies evolved into a transformative experience that reshaped our approach to business in unexpected ways.</p>


A month after launching, here's what we've learned

Lots done, a lot more to do. 1 month after launching, our analysis on how that went and what we learned.

<p>When we started building Zerve, we had a couple of perspectives on the space that we learned both through our own experience and through hundreds of interviews that we performed over the course of about a year. First, the tools available to data scientists force them to choose between stability and flexibility; i.e., exploring data in VS Code is a nightmare, and trying to deploy a notebook is practically delusional. Second, the only people generating value from data today are doing it with code. In other words, 100% of AI projects require code in one way or another.</p>
<p>That’s why we built Zerve, and that’s why we made it free for our users.</p>
<p>We launched on January 30 of this year and in the first week had over 1000s of people sign up to use Zerve. We got tons of great feedback directly and on social media, and that let us know that we are on the right track. Here are some things that we’ve learned along the way.</p>
<div>
<h3 id="d2696cc4c789"><br>The community recognizes the problem</h3>
</div>
<p>Everyone that signed up for Zerve got a direct email from me because we really want to understand their experience with Zerve and their needs in tooling. Going into the launch we thought we knew our users very well. We had run a small scale beta test earlier in 2023, and learned a lot through that experience, so this time we were looking for confirmation of what we’ve built.</p>
<p>We’ve gotten lots of great feedback. Things like, “Zerve AI is like Miro for developers, except cooler. 😎” and “It’s like the Google Colab upgrade we’ve all been waiting for.” These comments are particularly awesome as they would have been part of pitches we have given about our vision, but now we were seeing people we had never spoken to, see the problem and Zerve just like we did.</p>
<p>We have long since called the state of tooling in the data science space the “Data Science Stockholm Syndrome.” It definitely seems like people are waking up to the limitations of the tools. Since the seminal talk, “<a href="https://www.youtube.com/watch?v=7jiPeIFXb6U">I don’t like notebooks</a>,” posted in October of 2018 highlighting some of these issues, people have begun to realize that better tools are needed to do serious data science. Our launch further validated this.</p>
<div>
<h3 id="20f506e472c6"><br>Self-hosting is an absolutely critical part of data science</h3>
</div>
<p>Scores of data science tools have been built over the last decade in the cloud, and it always surprised me when they would stutter and stumble when asked about self-hosting. At the end of the day, nobody wants their data to have to leave their cloud environment and go to a third party to be analyzed or processed. Even more, vendors shouldn’t want this either. It creates all sorts of problems when it comes to data security and working with larger and more sophisticated organizations.</p>
<p>That’s why we built Zerve to be self-hosted from the beginning. We’re on the AWS marketplace, and a full self installation takes less than 10 minutes.</p>
<p>The feedback that we got from the launch (on our cloud environment) proved to us that people are very hesitant to send their data out to a third party, so we’ve had lots of conversations about getting people up and running in their own environments so that they can use Zerve for real projects on real data.</p>
<p>We also often hear that offering Zerve locally is important to the community. Zerve is, however, unlikely to be offering this. It’s not to diminish that this is important to some folks, for us it’s not how we see we’ll be able to elevate the impact of data science. For lots of reasons, but most crucially true collaboration, whether within the team or with stakeholders like Data Engineers - the benefits we have built are centred around streamlining the process of going from Prototype to Production using foundational cloud technologies to make handover, or direct deployment infinitely more straightforward.</p>
<div>
<h3 id="5345e998ebc1"><br>🐛 Bugs 🐞</h3>
</div>
<p>Of course there have been issues, but the community has a real sense of wanting to like what we are doing, and the patience shown to us to get things resolved has been really awesome. Additionally the community has quickly pointed out gaps in what we are doing. Some we knew about, and some new ones. It’s been insanely valuable to meet all of these people, understand their ‘why’ and look to add the desired functionality when it aligns with our vision.</p>
<p>&nbsp;</p>
<div>
<h3 id="cff59e37a03e">Generative AI is everywhere</h3>
</div>
<p>Early on, I thought that generative AI was largely hype from a business perspective, but I was completely wrong about that. Nearly everyone that we speak to is working on some sort of LLM or other generative AI project. Discovering and tapping into the capabilities of these powerful new models is a fundamental leap forward.</p>
<p>The bad news is that the tooling for fine-tuning and rolling out these models is very challenging. From the dev/ops issues related to utilizing GPUs and the costs associated with compute, many organizations have stumbled out the gate on these projects. Because Zerve provides easy, targeted, serveless GPU support, we resolve both of these issues pretty seamlessly.</p>
<p>That’s why next month we’ll be launching a host of new features including integrations with<span>&nbsp;</span><a href="https://huggingface.co/">HuggingFace</a><span>&nbsp;</span>and<span>&nbsp;</span><a href="https://aws.amazon.com/bedrock/">AWS Bedrock</a>, GPU support, and more. These features make Zerve incredibly well-positioned to be THE IDE for Gen AI.</p>
<p>Stay tuned for details of that launch on our website and our<span>&nbsp;</span><a href="https://www.producthunt.com/products/zerve-ai">Product Hunt page</a>.</p>{{ include_custom_fonts({"InterCustom":["Medium","Regular","Semi Bold"],"PPTelegraf":["Bold","Regular"]}) }}

<p>When we started building Zerve, we had a couple of perspectives on the space that we learned both through our own experience and through hundreds of interviews that we performed over the course of about a year. First, the tools available to data scientists force them to choose between stability and flexibility; i.e., exploring data in VS Code is a nightmare, and trying to deploy a notebook is practically delusional. Second, the only people generating value from data today are doing it with code. In other words, 100% of AI projects require code in one way or another.</p>


The Evolution of Data Science Environments

The Evolution of Data Science Environments: From Jupyter to the Future

An analysis of the history of notebooks, how they entered into the corporate world and a look to the future 

<div>
<h3 id="f296e506d91f"><strong>Introduction to Notebooks</strong></h3>
</div>

<p>The inception of interactive computational notebooks can be traced back to the 1980s with the development of Maple, Mathematica, and SageMath. These early versions were predominantly academic tools, designed to facilitate computational teaching and learning through a mix of code, visualizations, and narrative text. Mathematica's notebook interface inspired a new way of interactive computing, setting the stage for future developments. Fernando Pérez, deeply influenced by his experiences with Mathematica, initiated the development of IPython in 2001, laying the groundwork for what would become a significant shift in data science tools.</p>
<div><img src="https://20289263.fs1.hubspotusercontent-na1.net/hubfs/20289263/1-4.png" width="1125" height="750" loading="lazy" alt="1-4" style="height: auto; max-width: 100%; width: 1125px;"></div>
<div>&nbsp;</div>
<div>
<h3 id="4b2dcbc74450"><strong>Transition from Academia to Business</strong></h3>
</div>
<p>By 2007, the IPython team embarked on creating a notebook system, integrating text, calculations, and visualizations. This effort culminated in the release of the first version in 2011. The transformation of IPython into Project Jupyter in 2014 marked a pivotal moment, splitting the notebook functionality into a separate project. Jupyter's single kernel system design allowed it to serve a broad spectrum of scientific and research applications. Early data scientists, transitioning from academia, adopted Jupyter for its flexibility and interactive capabilities, despite its limitations for production environments.</p>
<p><img src="https://20289263.fs1.hubspotusercontent-na1.net/hubfs/20289263/2.jpg" width="1080" height="720" loading="lazy" alt="2" style="height: auto; max-width: 100%; width: 1080px;"></p>
<div>
<h3 id="36492d97a2f4"><strong>Evolution within the Corporate World</strong></h3>
</div>
<p>The evolution of Jupyter notebooks was significantly influenced by the open-source movement, with pivotal contributions like SciPy and Matplotlib laying the foundation for Python's scientific computing capabilities. These developments, coupled with Jupyter's ability to share and collaborate on python projects easily, highlighted its utility in academic and professional settings. However, the growing demand within the corporate world for projects to go beyond the exploratory limitations of a notebook meant that, in order to attempt to get their work into production, data scientists needed to move into some sort of IDE once they had completed their exploratory data analysis inside of a notebook. This often involved handing their work over to another data practitioner, such as a data engineer or MLops engineer, who would likely need to re-code the work in an IDE. The origins of this problem can be seen in the<span>&nbsp;</span><a href="https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giad113/7516267?login=false">un-reproducibility of research</a><span>&nbsp;</span>and data scientists coming from academia into the corporate world brought this reproducibility problem with them by bringing in the tooling they already knew.</p>
<div>
<h3 id="73efff04d94b"><strong>Emergence of the “Modern” notebook</strong></h3>
</div>
<p>Following this evolution within the corporate world, a new era of notebooks began to take shape. Cloud-based platforms such as Google Colabemerged, building upon Jupyter's foundational, single kernel architecture. These platforms attempt to modernise the data science development journey, with enhanced collaborative and interactive features. However, they are still in essence forks of the original Jupyter notebook and therefore pose the same problems when it comes to deployment.</p>
<div>
<h3 id="17744ebe292f"><strong>Bridging the gap</strong></h3>
</div>
<p>What has become clear is that as the field of data science has matured, the limitations of notebooks have become apparent. They are excellent for exploration and visualization but less suited for more extensive software development or production-level code. This led to a gap between the exploratory work done in notebooks and the development work done in traditional Integrated Development Environments (IDEs). Why this gap is more important today than ever before is because the world is demanding that AI and models make it into production. This gap is where <a href="https://www.zerve.ai/why-zerve" rel="noopener" target="_blank">Zerve</a>, a unified developer space for data science &amp; AI, was born. Zerve blends the exploratory flexibility of notebooks with the robustness and scalability of IDEs, enabled by its novel underlying architecture.</p>
<div class="hs-embed-wrapper hs-fullwidth-embed" data-service="youtube" data-responsive="true" style="position: relative; overflow: hidden; width: 100%; height: auto; padding: 0px; min-width: 256px; display: block; margin: auto;"><div class="hs-embed-content-wrapper"><div style="position: relative; overflow: hidden; max-width: 100%; padding-bottom: 56.25%; margin: 0px;"><iframe width="256" height="144.64" src="https://www.youtube.com/embed/FPqgFYXiPcE?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen title="Zerve's next generation data science development environment" style="position: absolute; top: 0px; left: 0px; width: 100%; height: 100%; border: none;"></iframe></div></div></div>
<p>&nbsp;</p>
<h3><strong style="font-family: var(--hsElevate--h3__font); font-size: var(--hsElevate--h3__fontSize); font-style: var(--hsElevate--h3__fontStyle); background-color: transparent; color: var(--hselevate--section--lightsection--1__textcolor);">The Subtle Integration of Advanced Features</strong></h3>
<p>With this novel architecture comes new benefits like language interoperability, parallelization capabilities, and cloud compute optimization. Such features represent the natural progression of data science development, addressing the growing demands for efficiency and collaboration in the field.<img src="https://20289263.fs1.hubspotusercontent-na1.net/hubfs/20289263/3-3.png" width="1200" height="800" loading="lazy" alt="3-3" style="height: auto; max-width: 100%; width: 1200px;"></p>
<h3><strong style="font-family: var(--hsElevate--h3__font); font-size: var(--hsElevate--h3__fontSize); font-style: var(--hsElevate--h3__fontStyle); background-color: transparent; color: var(--hselevate--section--lightsection--1__textcolor);">Looking Ahead: The Future of Data Science Environments</strong></h3>
<p>The progression of data science environments will be shaped by the burgeoning need for organizations to harness AI, including <a href="https://www.zerve.ai/ide-for-llms" rel="noopener" target="_blank">LLMs</a> and <a href="https://www.zerve.ai/blog/fine-tune-llama3" rel="noopener" target="_blank">generative AI</a>. This necessitates an efficient development process, where data science and AI teams can minimize their time to value and optimize the path from ideation to deployment. The evolution toward environments that integrate these advanced capabilities more deeply, support seamless collaboration, and enhance both development and deployment stages is crucial in meeting the escalating demands for leveraging AI technologies effectively.</p>
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<h3 id="f296e506d91f"><strong>Introduction to Notebooks</strong></h3>
</div>


The development environment built for code-first data users

Announcing Zerve - The Next Generation Data Science Development Environment

Release of Zerve v0.1 Data Science Development Environment

<p>Today we’re excited to be unveiling something totally new in the world of data science and AI development. We’ve been building in stealth for the last two years, and we're finally opening the door to let the world in.</p>
<div><img src="https://20289263.fs1.hubspotusercontent-na1.net/hubfs/20289263/1.png" width="858" height="572" loading="lazy" alt="1" style="height: auto; max-width: 100%; width: 858px;"></div>
<div>
<h3 id="9008cc2dd2bf">Our Mission</h3>
</div>
<p>Our mission at Zerve is to<span>&nbsp;</span><strong><em>Elevate the impact of Data Scientists</em></strong>. Why? A few reasons.</p>
<ol>
<li>My two co-founders Greg &amp; Jason, two PhD level statisticians, are two of the smartest people I know. They have tales of not being able to get their organization's AI initiatives over the line, or crazy inefficiencies in the process while doing so. They have lived the problems. Teams separately working on local notebooks and meeting twice a day to share findings, and emailing / zipping the latest data to other team members. Inefficient, and not fit for purpose in a world that will demand impact from Data Science teams like never before.</li>
<li>We’ve spent 100s of hours interviewing ‘Code-First Data Users’ who not only confirmed the efficiency losses we knew, but shedded light on even more. We heard all too often that the Data Scientist's impact was set up to be distinctly<span>&nbsp;</span><em>unimpactful</em>, a screenshot of a Matplotlib chart on a PPT, an uninspiring Data App, or a handover that resulted in a complete re-coding of the original work.</li>
<li>We believe to truly unlock the opportunities in AI, LLMs and Data Science over the next decade, expert Data Scientists will be the difference makers, and we set out to build a platform that puts Data Scientists at the heart of the innovation.</li>
</ol>
<div>
<h3 id="c55266ff82cb">The Trend Over the Last Decade</h3>
</div>
<p>The trend over the last decade has been moving towards low-code/no-code solutions for developing data science. Software vendors have developed a dizzying variety of tools that supposedly allow anyone to train sophisticated models and to interact with their data without needing to write code. It has become obvious to anyone who has actually ever deployed a Data Science project though, that these tools are wildly inflexible and therefore entirely unsuitable for doing anything serious. That’s because 100% of data science projects require code, and that’s why Zerve is built for code first data teams. The introduction of Large Language Models presents the opportunity to help build data solutions even further, however our thesis remains the same, these tools need to be used in addition to the knowledge of a code-first data user who is building the solution. We believe these tools will lead to even more code-first data teams, not less.</p>
<p>The coding toolkit for data, though, is problematic to say the least. Data engineers might be working in snowflake or DBT or some other tool where they write SQL. Passing the data along for analysis, the data scientists typically start in a notebook environment, like Jupyter. The problem with notebooks is that they are brittle. Jupyter was developed by academics to be used as a classroom scratch pad. Jupyter is wonderful for exploring data, but it’s undeployable - meaning that if you’re working on something serious, you’ll eventually need to move to an IDE, like PyCharm or VSCode at some point.</p>
<p>Because of the architecture of notebooks, it’s very easy to get them into a bad state. In the example below, by running the cells of the notebook too many times, it’s easy to get into a state where 1+1=7. That’s why every modern notebook has a “restart kernel” button. Because they all operate with a global variable space that makes production work with a notebook infeasible and dangerous.</p>
<div><img src="https://20289263.fs1.hubspotusercontent-na1.net/hubfs/20289263/2.png" width="1200" height="800" loading="lazy" alt="2" style="height: auto; max-width: 100%; width: 1200px;"></div>
<p>Since notebooks can’t handle production workloads, developers turn to scripting environments or IDE’s. These tools are terrific for writing stable code, but they were built for software developers and so they’re unsuitable for data exploration. Maybe that’s why the Jupyter plug in for VSCode has been downloaded over<span>&nbsp;</span><a href="https://visualstudiomagazine.com/articles/2022/07/12/python-vs-code.aspx">40 million times</a>.</p>
<p>Given the importance of where AI is at a board level, it's utterly surprising that this toolkit is involved in every serious data science project, at some point or another.</p>
<div><img src="https://20289263.fs1.hubspotusercontent-na1.net/hubfs/20289263/3.png" width="1200" height="800" loading="lazy" alt="3" style="height: auto; max-width: 100%; width: 1200px;"></div>
<p>That’s where Zerve comes in. Zerve is the first Data Science Development Environment of it's kind, built upon a novel architecture, giving you all the benefits of interactive analysis with notebooks without sacrificing the stability of a scripting environment. With Zerve you can explore, build, and deploy all from the same environment. To truly do this, the underlying architecture had to ensure stability. Let's explore that.</p>
<div>
<h3 id="f5183c726b40">The New Zerve Architecture</h3>
</div>
<p>The foundation of Zerve’s revolutionary architecture is separating storage from compute. In Zerve, code is organized as a DAG, and code executes from left to right. Each code block inherits the state of the preceding blocks. When you run your code in Zerve, Zerve launches serverless compute to execute your code. The state of the code block after code execution is then cached, serialized, and stored on disk so that it’s ready to be inherited by the next code block. This architecture changes the development dynamic in data science, and produces incredibly important benefits.</p>
<div>
<h3 id="c75c6ab9883e">Benefit: Stability</h3>
</div>
<p>There is no restart kernel button in Zerve. You read that correctly. That's because there is no way of getting your Zerve canvas into a bad state, so there is no need to ever restart it. Jupyter is a fantastic exploration and academic tool, but the fact that you can explore data is where the similarity stops with Zerve. No matter the bells and whistles that have been added to new cloud notebooks, they all share the same architectural flaw that makes them unsuitable for serious work.</p>
<p>The main innovation made by these tools is that they are cloud based, which makes collaboration easier. With these sorts of tools, it’s straightforward for an entire team to login to the same notebook and potentially run code and collaborate on a project together.</p>
<p>Unfortunately, this actually makes these environments even more unstable. Because of the global variable space of these tools, having multiple people all able to run code at the same time means that notebooks are constantly being put into a bad state, with kernels constantly needing to be restarted. In other words, innovation in the notebook space is moving in the wrong direction.</p>
<p>Zerve, on the other hand, has no restart kernel button because it’s impossible to get a Zerve canvas into a bad state. Zerve is guaranteed to produce a consistent result no matter how the code is executed. Whether in a single or multiplayer mode, this changes everything.</p>
<div>
<h3 id="fe3f4b780239">Benefit: True collaboration</h3>
</div>
<p>Supposedly collaborative notebook environments have been around for years, and really all the “innovation” happening in the notebook space involves collaboration in one way or another. Anyone who has used a collaborative notebook knows that the experience is pretty terrible. Because notebooks are brittle, having multiple users accessing the same notebook at the same time presents a number of problems that are extraordinarily difficult to solve given the architectural flaws with Jupyter.</p>
<p>In Zerve, though, the collaboration is real-time and doesn’t present these same challenges. Users can run as many blocks of code as they want at the same time, write in multiple languages simultaneously, leave comments and tag one another, and more. Because of Zerve’s architecture, none of these activities poses any problem to stability, and with our upcoming GitHub integration, version control and change management is easy.</p>
<div>
<h3 id="b06d7a80079f">Benefit: Language Interoperability</h3>
</div>
<p>Bouncing between tools is second nature to most data scientists and data engineers. SQL, R, and Python are written in different environments. It is our view however, that the need to do this, should be alleviated. The architectural flaws that exist in Data Science Development tooling here means the best that can be offered is to translate with packages like reticulate. This tends to be an offputting experience, and as a result an R coder just codes in Python, even when their skillset or the problem they are looking to solve, may be more suitable to R.</p>
<p>Because of the way that Zerve serializes the data from an executed block, different languages are suddenly able to utilize the same basic data types at the same time in the same project. For example, data frames are serialized as parquet files, so SQL, R, and python are all able to interact with these objects without having to do any complex and fragile code conversions. For v0.1, Zerve has built the architecture for mixing &amp; matching languages, but certain aspects are still in our alpha process, if you’d like early access, please email info@zerve.ai</p>
<p>In Zerve, data/ML engineers, R users, and python users can develop in the same environment at the same time. We think this is very cool.</p>
<div>
<h3 id="eba7c843f1bb">Benefit: Seamless multi-processing</h3>
</div>
<p>Parallelizing code execution can be a complicated process that most coding tools are not designed to handle without specific, detailed instructions. If you’re training 5 different models, most people will just allow them to train in sequence rather than go through the trouble of parallelizing the compute. Given the availability of cloud compute, this inefficiency is pretty alarming.</p>
<p>Zerve’s serverless technology allows users to run as many code blocks at the same time as needed. That means that cloud compute costs only accrue when code is actually running and there’s no need to worry about remembering to shut down servers. Zerve handles the orchestration flawlessly, saving both compute costs and clock time.</p>
<div>
<h3 id="432be0883975">Benefit: Persistent artifacts</h3>
</div>
<p>Moving a model from the design and development phase to production is a challenging task. Many new companies have sprouted up in the last few years around MLOps and hosting and maintaining models in production, and many companies have built their own procedures and pipelines for deploying their AI solutions. Things like managing dependencies and hosting models are outside the scope of knowledge of many data scientists.</p>
<p>Zerve’s automatically serializes all of the artifacts of every analysis by default. That means that the models and data sets created in a project are accessible from elsewhere in Zerve and from external applications as well.</p>
<div>
<h3 id="0b1630dbfc58">Start building in Zerve today!</h3>
</div>
<p>Zerve is free to use and our free plan includes significant storage and compute resources. It’s perfect for code-first data users. Whether you’re querying a database with SQL, visualizing data with R, or deploying models in python, Zerve can enhance your workflow, increase your productivity, and enable you to collaborate with friends and colleagues on all of your data projects.</p>{{ include_custom_fonts({"InterCustom":["Medium","Regular","Semi Bold"],"PPTelegraf":["Bold","Regular"]}) }}

<p>Today we’re excited to be unveiling something totally new in the world of data science and AI development. We’ve been building in stealth for the last two years, and we're finally opening the door to let the world in.</p>


The Journey to Unearthing the Pain Points in Data Science Development

Zerve's mission to increase the impact of Data Scientists

<p>Our mission at Zerve AI is crystal clear: to<span>&nbsp;</span><strong><em>Elevate The Impact of Data Science.</em></strong><span>&nbsp;</span>This mission was born out of the frustrations our team had in delivering data science projects at scale, but also shaped by the community of “code first data users”, we have spoken to along the way (friendships were fostered!). The journey began with a commitment to understanding the daily challenges faced by those on the front lines of data—Data Scientists, Data Engineers, and ML Engineers. Zoom after zoom, we invested 100s of hours interviewing and getting to know every person. We knew our mission affected us, but having spent time with the communtiy, we knew it was something larger.</p>
<div>
<h3 id="f5ba861bf13d">The Current Landscape</h3>
</div>
<p>Our conversations created a sobering reality—the state of data science tools is hindering the full potential of truly exceptional people. The impact they should be making is stifled by the barriers erected by current solutions. Despite awesome inputs, too often limited outcomes were achieved by the individual or the team.</p>
<p>However, it's a bell curve, and of course there are representations not depicted in this blog, but it is the view we saw expressed most, otherwise we would not have dedicated ourselves to this mission.</p>
<div>
<h3 id="98fbe18a69d3">Voices from the Field</h3>
</div>
<p>Although we have a team of statisticians, we didn't just want statistics; we wanted stories. The anecdotes and quotes collected during our interviews brought life to the pain points experienced by our interviewees. It was a moment of shared frustration and laughter when we presented a slide encapsulating the daily struggles we had previously identified from interviews, as there tended to be a realization that their work-life was mirrored heavily by other folks in similar shoes around the world. I wanted to show you the exact slide I shared, (despite my web team telling me it's "ugly"!). Here it is, and like I mentioned, it often sparked laughter due to the resonation with the viewer (and maybe the design!).</p>
<div><img src="https://20289263.fs1.hubspotusercontent-na1.net/hubfs/20289263/1-2.png" width="1031" height="687" loading="lazy" alt="1-2" style="height: auto; max-width: 100%; width: 1031px;"></div>
<div>
<h3 id="f1f43dd411fd"><br>The Pain Points</h3>
</div>
<div>
<h3 id="0703c841dca7">Getting Set Up</h3>
</div>
<p>One recurring theme echoed through the interviews was the substantial time spent getting set up in various systems at the beginning of an employment or at the start of a new project. Ensuring the right packages are used across a project was a constant pain point when it came to collaborating at exploration or handover stage. We heard of folks months into a job "ssh porting into a server to run a notebook" and describing it as "misery".</p>
<div>
<h3 id="47c3248e84a3">Communication Challenges</h3>
</div>
<p>Aligning with colleagues about the intricacies of a notebook or the nuances of the data they're working with becomes a bottleneck, hindering collaboration and progress. We heard stories of people who have added daily meetings to ensure Notebooks are run in the right order, teams waiting to make progress on a project while a colleague was on leave due to local data or code, or a quite remarkable story where slack emojis were used to manage the statuses of an online notebook. Better processes exist of course, and these are not a reflection of teams we met, but the circumstances they found themselves in organisationally, when BAU often made it difficult to rectify the origin of the situation.</p>
<div>
<h3 id="33ed9cb677ea">Deployment Hurdles</h3>
</div>
<p>Deploying models is not a solo endeavor; it requires aligning with software engineers, often getting on their roadmap, and explaining the intricate details. What we felt from the interviews here, was an often awkward handover process. Depending on the type of organisation, the pain here could be felt on different levels, with frustrations occurring on both the engineering and data science side. Machine Learning, AI or Analytics Engineers are brilliant, but finite resources for working between the two disciplines.</p>
<div>
<h3 id="62482468e57c">Resource Configuration</h3>
</div>
<p>Configuring cloud and compute resources shouldn't be a roadblock. However, for many, it is. The inability to easily configure these resources themselves—or sometimes not at all—adds layers of complexity, involving additional teams and further delays. They end up as a ticket for another team, and yet again not in complete control of the level of impact they can make.</p>
<div>
<h3 id="c8498c0ae0be">Demonstrating Work</h3>
</div>
<p>The struggle to demonstrate their findings is real. Whether it's the manual process of copying and pasting visualisations into PowerPoint or wrestling with a BI tool, the inefficiency in showcasing their work remains a significant pain point. The key problem here is that the lack of impact is felt across the board on this one. We had a CDO say “I thought I hired coders, but all I have 'gotten' is screenshots of charts in a PPT”. Of course the content of that presentation was all built with code, however the organisation had not given the team an effective way to share, via data apps, or integrations with core systems. It's just not where any organisation needs to be today.</p>
<p>In the next section, we'll explore how these pain points extend beyond individual experiences and affect the broader landscape of data science teams and their perceived value within organisations.</p>
<div>
<h3 id="f24a62f20bf3">Leaders' Perspectives</h3>
</div>
<p>The pain points identified in our interviews weren't isolated incidents. Leaders across organizations echoed a common sentiment: they weren't receiving the expected value from their data science teams. This sentiment reverberated through discussions with executives and managers who, time and time again, expressed dissatisfaction with the outcomes of their data science initiatives.</p>
<p>These leaders emphasized the gap between the immense talent within their data science teams and the tangible impact on business objectives. The disconnect was stark, with data scientists sometimes struggling to translate their high-level statistical work into solutions that could be efficiently integrated into existing workflows. This hit home really hard for the team at Zerve. The frustrations we felt coming back from the executive team was they had been misguided with their original hire, the data scientist. They were made to believe they originally hired something different to what they got, when in fact, the fault was organisational and there was not a road for delivering impactful results from the data scientist, and that’s why no impact was seen by the executive team.</p>
<div>
<h3 id="684358385f85">Enter Zerve</h3>
</div>
<p><a href="https://www.zerve.ai/blog/Announcing%20Zerve%20DSDE%2030124">Check out this post where we launch our solution</a><span>&nbsp;</span>in response to the information we received in the interviews above.</p>{{ include_custom_fonts({"InterCustom":["Medium","Regular","Semi Bold"],"PPTelegraf":["Bold","Regular"]}) }}

<p>Our mission at Zerve AI is crystal clear: to<span>&nbsp;</span><strong><em>Elevate The Impact of Data Science.</em></strong><span>&nbsp;</span>This mission was born out of the frustrations our team had in delivering data science projects at scale, but also shaped by the community of “code first data users”, we have spoken to along the way (friendships were fostered!). The journey began with a commitment to understanding the daily challenges faced by those on the front lines of data—Data Scientists, Data Engineers, and ML Engineers. Zoom after zoom, we invested 100s of hours interviewing and getting to know every person. We knew our mission affected us, but having spent time with the communtiy, we knew it was something larger.</p>


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