Scalable Machine Learning with Apache Spark

This 2-day Machine Learning with Apache Spark training will teach you how to scale ML pipelines with Apache Spark™, including distributed training, hyperparameter tuning and inference. You’ll build and tune ML models with SparkML while leveraging MLflow to track, version and manage these models. We’ll cover 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

Objectives

• 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

Prerequisites

• Intermediate experience with Python
• Experience building machine learning models
• Familiarity with PySpark DataFrame API