The AI-Powered Super IC
Building Reliable AI Systems with MLflow
Hosted by Maria Vechtomova, Başak Eskili, and Daniel Liden
What you'll learn
Prototype and evaluate your AI application with MLflow
Understand how you can use MLflow tracing and evaluation to build a reliable AI application.
Build and deploy your application on Databricks
Understand how you can use a custom model to log and register your AI application, and then deploy it on Databricks.
Why this topic matters
GenAI projects tend to get complicated quickly. They often have many components, including both deterministic functions and GenAI model calls, and use models from multiple different providers.
In this session, we show you some basic MLflow GenAI functionality that will simplify the process of building and maintaining your AI applications.
You'll learn from
Maria Vechtomova
MLOps Tech Lead, Databricks MVP
MLOps Tech Lead with 10+ YoE in machine learning and MLOps. Databricks MVP. Writing an O'Reilly book on MLOps with Databricks.
Başak Eskili
ML Engineer
ML Engineer with 6+ YoE. Currently working at booking.com in a feature store team, serving 200k RPS with P99.9 25ms latency.
Daniel Liden
Senior Developer Advocate at Databricks
Daniel is a Developer Advocate at Databricks, focusing on MLflow.
Go deeper with a course
End-to-end MLOps with Databricks
Maria Vechtomova and Başak Eskili
MLOps Tech Lead | Databricks Beacon | 10+ years in Data & AI. Senior ML Engineer | 7+ years in Data & AI
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