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Scalable Machine Learning with Apache Spark™

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This course teaches you how to scale ML pipelines with Spark, including distributed training, hyperparameter tuning, and inference. You will build and tune ML models with SparkML while leveraging MLflow to track, version, and manage these models. This course covers the latest ML features in Apache Spark, such as Pandas UDFs, Pandas Functions, and the pandas API on Spark, as well as the latest ML product offerings, such as Feature Store and AutoML.

This course will prepare you to take the Databricks Certified Machine Learning Associate exam.


2 full days or 4 half days


  • Perform scalable EDA with Spark

  • Build and tune machine learning models with SparkML

  • Track, version, and deploy models with MLflow

  • Perform distributed hyperparameter tuning with HyperOpt

  • Use the Databricks Machine Learning workspace to create a Feature Store and AutoML experiments

  • Leverage the pandas API on Spark to scale your pandas code



Day 1

  • Spark / ML overview

  • Exploratory data analysis (EDA) and feature engineering with Spark

  • SparkML: transformers, estimators, pipelines, and evaluators

  • MLflow Tracking and Model Registry

Day 2

  • Parallelizable hyperparameter tuning

  • Databricks AutoML and Feature Store

  • Integrating 3rd party packages (distributed XGBoost)

  • Distributed inference of scikit-learn models with pandas UDFs

  • Distributed training with pandas function API

  • Pandas API on Spark for data manipulation

Upcoming Public Classes

Public Class Registration

If your company has purchased success credits or has a learning subscription, please fill out the public training requests form. Otherwise, you can register below.

Private Class Delivery

If your organization would like to request a private delivery of the course, please fill out the request form below.


If you have any questions, please refer to our Frequently Asked Questions page.