Scalable Machine Learning with Apache Spark™ (V2)
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.
- Intermediate experience with Python (or completion of Introduction to Python for Data Science & Data Engineering)
- Familiarity with PySpark DataFrame API (or completion of Apache Spark Programming)
- Experience building machine learning models
Outline
M1: Exploring Data
M2: Linear Regression
M3: MLflow
M4: AutoML
M5: Decision Trees
M6: Random Forests and Hyperparameter Tuning
M7: Hyperopt
M8: Feature Store
M9: XGBoost
M10: Pandas
M11: Lab Walkthroughs
Self-Paced
Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos
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Databricks has a delivery method for wherever you are on your learning journey
Self-Paced
Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos
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Instructor-Led
Public and private courses taught by expert instructors across half-day to two-day courses
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Blended Learning
Self-paced and weekly instructor-led sessions for every style of learner to optimize course completion and knowledge retention. Go to Subscriptions Catalog tab to purchase
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Skills@Scale
Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details
Upcoming Public Classes
Career Workshop/
March 20
