Diagnose ML systems and debug data pipelines

Learn how to diagnose ML systems and stop blaming the model when predictions fail. Mastering pipeline debugging is essential for product managers to own their AI roadmap. When an AI product breaks, average managers ask data scientists to tweak model weights. Great product managers trace the data backwards to find the broken valve. This lesson uses a plumbing metaphor to explain pipeline debugging, showing you how to check output predictions, inspect middle features, and audit raw training data. We walk through a fraud model example where an app update sent blank values that became zeros in the feature store. Instead of lowering the threshold to mop the puddle, you will learn to map data lineage from the event tracker to the model and fix the root cause. Always demand a data lineage map before launch. In this lesson: - Pipeline debugging concepts - Tracing features to the source - Fraud model data lineage example - Demanding data lineage maps U2xAI Academy - AI skills for product managers.