Week 6 Review: Data Quality and Monitoring Foundations
Wire Validation, Drift Checks, and a Monitoring Plan Into One Pipeline That Refuses to Train on Bad Data.
MLOps106
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Introduction to Model Monitoring Concepts
Know Exactly What to Watch in Production: Data Quality, Drift, Model Performance, and System Health, Each With a Threshold and an Owner.
Data Drift Detection Fundamentals
Catch Production Data Drifting Away From Your Training Data With KS, PSI, and Chi-Squared Tests Before Accuracy Quietly Drops.
Data Validation with Great Expectations
Write Your Data Assumptions as Tests, Run Them on Every Batch, and Stop the Pipeline When They Break.
Feast Online Store and Real-Time Feature Serving
Serve Feast Features in Milliseconds, Keep Them Fresh, and Wire Them Into Your FastAPI Model at Inference Time.
Feature Stores: Concepts and Use Cases
Understand How Feature Stores Organize, Reuse, and Serve ML Features for Training and Real-Time Predictions.