PySpark - Module 6.0: MLlib Fundamentals
MLlib fundamentals: the Pipeline API and feature engineering on Databricks
PySpark - Module 6.1: Supervised Learning at Scale
Supervised learning at scale: trees, ensembles, evaluation, and tuning with CrossValidator
PySpark - Module 6.2: Unsupervised Learning and Recommendation
Unsupervised learning and recommendation: K-Means clustering and ALS collaborative filtering
PySpark - Module 6.3: ML Pipelines in Production
ML pipelines in production: saving models, batch inference, MLflow tracking, and drift