01Module 10 - ML System DesignEnd-to-end ML system design - from problem framing through deployment, feedback loops, and responsible AI. Master the skills that separate ML engineers who ship from those who only experiment.02Framing ML Problems - Turning Business Goals into Training ObjectivesLearn how to translate ambiguous business goals into precise ML objectives - the most critical and most overlooked skill in ML system design.03Data Collection Strategy - Building the Moat Before Training the ModelLearn how to design data collection and labeling strategies that determine a model's fate before a line of training code is written - the most underestimated skill in ML engineering.04Feature Engineering at Scale - The 80% of ML Work That Determines 80% of ResultsHow to build feature pipelines that work identically in training and serving - feature stores, point-in-time joins, crossing, embedding lookup, and avoiding training-serving skew.05Model Selection Strategy - Choosing the Right Model for the Right ProblemA systematic framework for selecting model families, managing complexity budgets, tuning hyperparameters, and knowing when AutoML helps versus hurts.06Offline vs Online Evaluation - Why Your AUC Goes Up But Revenue Goes DownA deep dive into offline and online evaluation strategies, A/B testing fundamentals, sample size calculation, interleaving, and the root causes of the offline-online metric gap.07ML Deployment Patterns - From Jupyter Notebook to Production at ScaleA comprehensive guide to ML deployment strategies, serving architectures, optimization techniques, and model registry practices for shipping models safely at scale.08Feedback Loops and the Data Flywheel - How ML Systems Compound Over TimeA deep dive into feedback loop design, concept drift detection, retraining strategies, and building data flywheels that make ML systems continuously improve in production.09Responsible AI and Ethics - Building Systems That Don't Cause HarmFairness metrics, bias detection, privacy-preserving ML, model auditing, and the regulatory frameworks every ML engineer must understand.
