Managing the full deployment lifecycle of TensorFlow models with the MLflow Model Registry (sponsored by Databricks)

MLflow is an open source platform to manage the machine learning lifecycle, including experiment tracking, reproducible runs, and model packaging. Clemens Mewald offers an overview of the latest component of MLflow, a model registry that provides a collaborative hub where teams can share ML models, work together from experimentation to online testing and production, integrate with approval and governance workflows, and monitor ML deployments and their performance. You’ll learn how to manage the full deployment lifecycle of TensorFlow models, from training to staging, A/B testing, and deployment to TensorFlow Serving.

This session is sponsored by Databricks.