#MLlib

Learn to operationalize Apache Spark MLlib models using SAS Model Manager and Workflow Manager. Discover how to automate and govern the entire model lifecycle, from building to monitoring, accelerating deployment while maintaining control.

Explore Random Decision Forests on Apache Spark with Sean Owen. Learn about decision trees, feature vectors, model interpretation, and hyperparameter tuning for machine learning in distributed environments.

Learn to build scalable machine learning solutions using Apache Spark, MLlib, MLflow, TensorFlow, and PyTorch. Explore distributed computing concepts, manage ML lifecycles, and combine Spark with deep learning for end-to-end workflows.

GPU-accelerated machine learning in Spark using RAPIDS ML, offering significant speedup and cost savings over traditional MLlib implementations for large-scale distributed ML applications.