Learning Series | Machine Learning Model Deployment

Tushar_Parekar — Fri, 15 May 2026 17:17:01 GMT

Databricks Academy offers the free Machine Learning Model Deployment course to help machine learning practitioners understand and apply common deployment strategies on the Databricks Data Intelligence Platform.

As the third course in the “Machine Learning with Databricks” series, it focuses on practical ways to move models from development into production using Databricks tools and workflows.

You’ll learn to:

Understand core deployment strategies: Learn the differences between batch, pipeline, and real-time deployment, and when each approach makes the most sense.
Run batch and pipeline inference on Databricks: Explore how to use Databricks features such as DLT and related workflows to deploy models in scheduled and pipeline-based scenarios.
Deploy models for real-time use cases: See how Model Serving and serving endpoints support low-latency inference for applications that need real-time predictions.
Evaluate deployment trade-offs and platform capabilities: Understand the strengths, limitations, and MLflow deployment features that help support model deployment on Databricks.

Designed for:

ML practitioners who want practical experience deploying models on Databricks
Learners with basic Databricks and MLflow experience and familiarity with model training, evaluation, and inference concepts
Users comfortable with Python, Spark, Delta Lake, Unity Catalog, and feature engineering fundamentals

Course format & details:

Syllabus: 4 sections | 19 lessons
Duration: About 2 hours
Skill level: Associate
Includes labs: No
Cost: Free

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topic Learning Series | Machine Learning Model Deployment in Announcements

Learning Series | Machine Learning Model Deployment