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.
Link to repo: github.com/koernigo/databricksMLOpsAzureDemo Databricks is proud to announce that Gartner has named us a Leader in both the 2021 Magic Quadrant for Cloud Database Management Systems and the 2021 Magic Quadrant for Data Science and Machine Learning Platforms. Download the reports here. databricks.com/databricks-named-leader-by-gartner
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- LLMOps is Here! I Ran Dolly in 10 Lines of Code with MLflow on Azure Databricks ( Download)
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- MLOps explained | Machine Learning Essentials ( Download)
- Introducing MLflow for End-to-End Machine Learning on Databricks ( Download)
