Databricks Academy offers the free Machine Learning at Scale course to help machine learning practitioners understand how Apache Spark supports ML workloads on Databricks. As the first course in the Advanced Machine Learning series, it focuses on practical ways to use Spark for data preparation, model training, tuning, deployment, and scalable inference.
You’ll learn to:
- Understand Spark for machine learning: Learn how Spark architecture supports ML workloads and when it makes sense to use Spark across the ML lifecycle.
- Build and tune models at scale: Use Spark ML for data preparation, training, and evaluation, and explore scalable hyperparameter tuning with Optuna and Spark.
- Package and govern models on Databricks: See how MLflow and Unity Catalog support model tracking, packaging, and governance in production-ready ML workflows.
- Work with Spark for deployment and pandas workflows: Understand how Spark supports model deployment, scalable inference, and pandas APIs on Spark for larger workloads.
Designed for:
- Machine learning practitioners working with larger-scale ML workloads on Databricks
- Users with intermediate Python experience and familiarity with common ML libraries like pandas, numpy, and scikit-learn
- Learners with basic Spark, MLflow, SQL, and distributed computing knowledge
Course format & details:
- Series: First course in the Advanced Machine Learning series
- Syllabus: 4 sections | 24 lessons
- Duration: 2 hours
- Skill level: Professional
- Cost: Free
- Includes labs: No
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