Tech Talk | MLOps on Azure Databricks with MLflow

This session will illustrate and demonstrate how Databricks' managed MLflow and the Azure ecosystem can be used to effectively implement an integrated MLOps lifecycle for managing and deploying Machine learning models.

The focus will be on the MLflow Model Registry, a centralized model store, set of APIs and a UI to collaboratively manage the full lifecycle of a machine learning model. We'll provide a detailed preview of the MLflow Registry Webhooks feature which allows for the automated triggering of MLOps pipelines.

Speaker:

**Oliver Koernig, Solutions Architect, Databricks

Oliver Koernig is a Solutions Architect at Databricks. He has spent the past 10 years focussing on Big Data and Machine Learning with a heavy focus on the Financial Services Industry.

Hosted by:

** Jules Damji, Developer Advocate, Databricks

Jules S. Damji is a Developer Advocate at Databricks and an MLflow contributor. He is a hands-on developer with over 15 years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, @Home, Opsware/Loudcloud, VeriSign, ProQuest, and Hortonworks, building large-scale distributed systems. He holds a B.Sc and M.Sc in Computer Science (from Oregon State University and Cal State, Chico respectively), and an MA in Political Advocacy and Communication (from Johns Hopkins University).