Managing Models Using MLflow on Databricks
Released 6/2026
By Yasir Khan
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 1h 9m 7s | Size: 161.9 MB
Managing machine learning models in production requires more than training accurate algorithms.
Managing machine learning models in production requires more than training accurate algorithms.
In this course, Managing Models Using MLflow on Databricks, you'll gain the ability to manage end-to-end MLOps workflows using MLflow and Databricks.
First, you'll explore how to configure MLflow Tracking, log experiments and model runs, and register models using Unity Catalog.
Next, you'll discover how to manage model versions, apply governance and metadata, automate lifecycle workflows, and validate models using MLflow and Databricks APIs.
Finally, you'll learn how to deploy models using Mosaic AI Model Serving, manage serving endpoints, score models through REST APIs, and monitor deployed models in production.
When you're finished with this course, you'll have the skills and knowledge needed to manage the complete machine learning lifecycle in Databricks using modern MLflow-based MLOps practices.
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