Recommender Systems Workload (Ray Train)

Learn how to build a scalable matrix factorization recommendation system on the MovieLens 100K dataset and migrate it from a local workflow to distributed, fault-tolerant training on an Anyscale cluster using Ray Train V2 and Ray Data. You’ll prepare the dataset (CSV/Parquet) and run end-to-end collaborative filtering training to predict user–item ratings.

• e Recommendation system pattern with Ray Train
• Imports
• Define matrix factorization model
• Define Ray Train loop (with validation, checkpointing, and Ray-managed metrics)
• Resume training from checkpoint
• Inference: recommend top-N items for a user