Learn how Zerve's agentic development environment helps you focus on the interesting parts of data work. Watch tutorials, demos, and real examples from teams shipping faster with production-grade workflows.
Filter by
In this episode, Greg will be joined by Roey Zalta, an AI leader working at the enterprise and government level. We’ll talk about what it actually means for a company to become “agentic,” how AI systems are being deployed in production, and what the real risks and opportunities look like over the next few years. This one moves from technical implementation to big-picture implications.
In this episode, Greg Michaelson sits down with Thomas Dinsmore, a longtime industry veteran with deep experience in competitive strategy, enterprise data science platforms, and the evolution of AutoML. Together, they will explore how the data science landscape has changed over the past decade and how the emergence of generative AI is influencing the way organizations think about building and deploying models. The conversation will touch on governance, reproducibility, and scale in real-world enterprise environments, as well as broader questions around hype cycles, trust in automation, and where human judgment still plays a critical role. Listeners can expect a thoughtful, grounded discussion that places today’s GenAI moment in a longer historical and practical context, without assuming simple answers or clear winners.
In this episode, Greg Michaelson is joined by Razi Raziuddin for a wide-ranging conversation about how data science is evolving as automation and agent-based systems become more capable. They will discuss why feature engineering and data preparation dominate the data science lifecycle, how organizations balance productivity with model performance, and where tools like AutoML, feature stores, and large language models fit in real-world practice. The conversation will also explore go-to-market challenges, the gap between hype and adoption, and how teams are rethinking what it means to build and deploy predictive models at scale.
In this upcoming conversation, Greg reconnects with Andrew Engel to reflect on the winding paths many data scientists have taken over the past decade. Expect a wide-ranging discussion touching on the evolution of the AI ecosystem, lessons learned from B2B SaaS and consulting models, and what happens when experienced practitioners step away from established companies to build something new. The episode will also explore how emerging tools like large language models are reshaping work, startups, and decision-making, alongside broader reflections on remote work, product-market fit, and building technology with real human impact.
In this episode, Greg sits down with longtime colleague and founder Satadru Sengupta for a wide-ranging conversation about the evolution of data science, startups, and automation. Drawing on experiences from early AutoML days through building consumer-facing platforms, the discussion will explore how real-world constraints shape product design, why workflow matters more than point solutions, and how emerging agent-based systems are changing how software gets built and used. Expect reflections on startup uncertainty, lessons from failure, and perspectives on where applied AI is headed next.
Join Zerve co-founders Greg Michaelson and Jason Hillary for a fully interactive, hands-on code-along where you’ll build an end-to-end optimization project from scratch using Zerve’s new notebook view. You’ll write code with us step by step, ask questions as you go, and learn how to model a synthetic city, generate EV charging demand, define an optimization objective, and use heuristic search to place charging stations efficiently. This session is designed for active participation: follow along, experiment, and get real-time guidance from two experienced PhD data scientists. Everyone who attends gets free Zerve credits so you can continue exploring and applying these techniques long after the workshop. Key takeaways from attending this event: How to translate a real-world planning problem into an optimization objective (for example, constructing a weighted distance function and defining constraints like service radius.) How to implement and iterate on heuristic optimization methods (such as k-medoids–style search) to improve facility placement without relying on packaged solvers. How to build and visualize an end-to-end optimization workflow in Zerve, including generating synthetic spatial data, evaluating solutions, and comparing baseline vs optimized layouts. Get ready to have some fun!
Two Data Robot alumni. Decades of combined experience in AI and customer success. One conversation. John Forrest joins Greg Michaelson on Data Day. John has built his career leading technical support at companies like Netezza, Data Robot, and now Qdrant, where he oversees customer success for one of the leading vector database platforms. Expect a candid conversation between two industry veterans about where AI has been, where it's going, what it takes to build lasting customer relationships, and lessons learned from years of solving hard problems for demanding customers.
In this livestream, Greg Michaelson will be joined by longtime friend and fellow data scientist Ray M. for a wide-ranging conversation about how AI is reshaping both work and everyday life. They’ll talk about Ray’s path from traditional risk analytics into the startup AI world, how he thinks about data-driven decision making, and what “AI native” students, companies, and tools might look like. Expect a candid discussion about large language models, agentic workflows, education, privacy, and what all of this could mean for the next generation of data scientists. If you’re trying to understand where AI is heading and how to stay relevant, this livestream will be worth tuning into.
Greg Michaelson, Zerve’s Co-Founder and Chief Product Officer, will sit down with longtime friend and seasoned ML engineer Dennis Oleksyuk, co-founder and CTO of AirCon.ai. Together they'll dig into the real, unglamorous engineering behind agentic AI systems, far removed from demos, hype cycles, and toy examples.
