Every ML team knows the pain: the majority of practitioner time goes into data wrangling, pipeline plumbing, and environment configuration — not solving business problems. Getting a pandas prototype into production still takes months, and that’s before you factor in monitoring, drift, and retraining. Agentic ML offers a fundamentally different approach: AI agents that understand your data context, reason about which steps to take, and execute ML workflow stages autonomously — while keeping humans in the loop for strategic decisions. In this talk, we explore why Agentic ML is a paradigm shift beyond AutoML, what makes context-aware agents effective, and how these ideas connect to Python workflows in practice. We’ll walk through real patterns for feature engineering, distributed training, and model monitoring — with honest lessons about where agents shine and where they still fall short. You’ll leave with a practical framework for thinking about agent-assisted ML in your own stack.
ML practitioners still spend most of their time on data preparation and pipeline plumbing rather than modeling. Agentic ML offers a different response: agents that operate in a continuous observe-reason-execute-evaluate loop with deep awareness of your data context — unlike AutoML (no reasoning) or code-generation chatbots (no execution).
In this session, we cover:
Attendees will leave with a framework for where agents add value, where human judgment remains essential, and how to introduce agent-assisted workflows into their Python ML stacks
Senior AI/ML Architect, Applied Field Engineering, Field CTO at Snowflake
Senior AI/ML Architect, Applied Field Engineering, Field CTO at Snowflake
