AI-Assisted ML Engineering: Making Production ML Accessible with Cursor, MCP, & Tecton

The barrier to production ML has been lowering for years, but it's still a significant undertaking and out of reach for many teams. ML engineers have to overcome talent shortages, legacy tech stacks, and implementation hurdles. But that's about to change.

In this webinar, Tecton CTO and Co-Founder Kevin Stumpf will show how we've integrated Tecton with Cursor and MCP to enable AI-assisted feature engineering directly from your IDE. Imagine describing your feature requirements in plain English - "Build a feature that divides the 10min transaction average by the 30d transaction average amount. I need ~200ms feature freshness" - and watching as an AI agent writes the code, builds a unit test, validates the syntax, and fixes any errors autonomously.

Kevin will cover:

• The architecture behind our MCP integration and how it enables AI agents to browse Tecton's code snippet library and API references
• A live demo showing the control loop in action: from natural language to production-ready feature pipelines in seconds
• Why declarative, "as-code" platforms are perfect for AI agents (and why drag-and-drop UIs would fail)
• Surprising lessons we've learned: how LLM progress is eating boilerplate prompt hacks, why public docs are critical, and why agent confusion is your best DX feedback
• Why test-driven development is more important than ever in the age of AI-assisted coding

For teams struggling with ML talent shortages, trapped in legacy systems, or facing year-long timelines for better infrastructure, AI-assisted ML engineering represents a major breakthrough. Join to see firsthand how this technology is making sophisticated real-time ML accessible to a much wider audience of engineers and companies.