Course Description
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
Course Summary
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Course Modules
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
Testimonials
Absolutely loved the enthusiasm and appreciate the knowledge he brought to class!!!
- Shelly Fruits, KPERS
“Instructor was very passionate about Databricks and helped me to stay engaged. Great pace, great knowledge, and the trainer was fantastic overall :) ”
- Emma Darling, Platform Engineer, Barclays Bank
“The Databricks Training was excellent”
- Spencer Martin, VP, Information Systems, RoyOMartin
