MLOps with Databricks
MLOps with Databricks
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 9m | 166 MB
Instructor: Maria Vechtomova
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 9m | 166 MB
Instructor: Maria Vechtomova
In this course, MLOps expert Maria Vechtomova introduces the components and principles that you must understand to successfully deploy machine learning models to production on Databricks. Dive into the step-by-step process of using Feature Engineering in Unity Catalog, tracking model experiments in mlflow, registering a model in Unity Catalog, and deploying your model using Databricks model serving. Explore the use cases where Feature Serving can be used and find out how to deploy a Feature Serving endpoint. Plus, learn how to package your code, deploy your project using Databricks Asset Bundles, and monitor your ML application using inference tables and Lakehouse monitoring.
Learning objectives
- Explain main components and principles required to deploy machine learning models to production on Databricks.
- Identify how to use experiment tracking system, model registry, feature engineering, model/feature serving, and other features required to deploy ML applications.
- Articulate how to package your Python code using best practices and deploy your project using Databricks Asset Bundles.
- Review how to monitor your ML applications.
MLOps with Databricks
