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 โMachine Learning with Databricksโ series, it focuses on practical ways to move models from development into production using Databricks tools and workflows.
Youโll 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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