Data Engineering Central Podcast

What does it actually take to build AI systems that work in production?In this episode of Data Engineering Central, I sit down with Maria Vechtomova, AI Engineering Lead, co-founder of CAUCHY, Databricks MVP, and author of MLOps with Databricks.Maria has spent more than 12 years working across data, machine learning, MLOps, and AI engineering. We talk about how production ML has evolved from the early days of homegrown model platforms to today’s world of LLMs, agents, Databricks, MLflow, and AI-assisted coding.We dig into why LLM applications can be significantly harder to evaluate and monitor than traditional ML, the growing importance of tracing and observability, and why governance becomes more complicated when agents can interact with data and other systems. Maria also explains why many of the problems we’re facing with AI aren’t actually new — AI has simply made them much harder to ignore.* We also get into AI coding tools, testing AI-generated code, Databricks and Unity Catalog, the rapidly changing LLMOps ecosystem, and what engineers should focus on as AI changes the way software is built.One of my favorite ideas from the conversation: AI is a mirror. It can help you write a lot more code, a lot faster — but whether that code is actually good still depends on the engineer behind it.Maria also shares what she thinks matters most for engineers entering the field today: common sense, soft skills, and business sense.Topics include: MLOps and LLMOps, production AI, Databricks and MLflow, AI agents, observability and tracing, model and LLM evaluation, AI governance, Unity Catalog, AI-generated code, testing, and the future of AI engineering.Thanks for reading Data Engineering Central! This post is public so feel free to share it. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit dataengineeringcentral.substack.com/subscribe