Debugging Machine Learning Code: Diagnose, Trace, and Fix ML Pipelines

Machine learning systems fail in unique and often silent ways that traditional software debugging tools cannot catch. A pipeline might run without throwing a single error, yet still produce completely useless predictions due to subtle data drift, schema mismatches, or training anomalies. This text-based course equips you with the foundational framework and practical strategies needed to systematically diagnose, trace, and fix modern machine learning workflows. Through clear written explanations and structured code walkthroughs, you will transition from guessing why a model is underperforming to confidently isolating the root cause of pipeline failures. You will start with core debugging concepts before moving on to practical techniques for data validation, training diagnostics, and model evaluation. What you'll learn: - Understand the unique failure modes of machine learning systems compared to traditional software. - Trace and resolve common tensor shape mismatches and numerical errors in Python pipelines. - Validate incoming data schemas to prevent pipeline breaks and catch silent data corruption. - Diagnose training anomalies, including overfitting, underfitting, and gradient issues. - Evaluate model performance using robust metrics to ensure reliability before and after deployment. This course begins with essential terminology and structural concepts, gradually guiding you through real-world debugging scenarios. It is designed for developers, data scientists, and engineers who have a basic understanding of Python and want to master the art of troubleshooting machine learning systems. No prior experience with advanced ML debugging is required. Start mastering the art of troubleshooting machine learning systems today.