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.
What you'll learn
- Learn how MLflow supports the full machine learning lifecycle
Clemens Mewald is the director of product management, machine learning and data science at Databricks, where he leads the product team. Previously, he spent four years on the Google Brain team building ML infrastructure for Google, Google Cloud, and open source users, including TensorFlow and TensorFlow Extended (TFX). Clemens holds an MSc in computer science from UAS Wiener Neustadt, Austria, and an MBA from MIT Sloan.
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