Scalable Machine Learning with Apache Spark™

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.

Skill Level

Associate

Duration

16h

Prerequisites

Intermediate experience with Python, Experience building machine learning models, Beginner experience with PySpark DataFrame API

Outline

Day 1

Spark / ML overview
Exploratory data analysis (EDA) and feature engineering with Spark
Linear regression with SparkML: transformers, estimators, pipelines, and evaluators
MLflow Tracking and Model Registry

Day 2

Tree-based models: Hyperparameter tuning and parallelism
HyperOpt for distributed 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

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Upcoming Public Classes

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