MLOps on Azure Databricks with MLflow

In this session, Oliver Koernig, a Solutions Architect at Databricks, 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.

Oliver will focus 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 and he will provide a detailed preview of the MLflow Registry Webhooks feature which allows for the automated triggering of MLOps pipelines.

[Code Repo


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