Using ML flow and Databricks to deploy ML models in Production

Using ML flow and Databricks to deploy ML models in Production by Ali Sezer

The emergence of Machine Learning Operations – how artificial intelligence has become a priority for most companies and how the ML Flow framework coupled with Databricks Data Science offering is helping teams build reliable systems for delivering models into production. This session will cover how you can implement the ML Flow framework to manage your model development process end-to-end, have a complete view of the models you have been training, and how you can structure a production-grade model deployment strategy. It will also cover some of the complimentary tools and features offered by Databricks such as the Feature Store and AutoML.

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