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Module 12 6 concepts
ML Systems & Production
MLOps, deployment, monitoring, and production ML.
01 02 03 04 05 06
A/B Testing for ML
Comparing model versions in production with statistical rigor – offline metrics don’t always predict online impact.
Data Drift and Model Monitoring
Detecting when production data diverges from training data – models degrade silently without monitoring.
Experiment Tracking
Logging parameters, metrics, artifacts, and code versions – reproducing results and navigating the experiment space systematically.
ML Pipelines
Chaining data processing, feature engineering, and model training into reproducible, deployable workflows.
Model Deployment and Serving
Batch vs. real-time inference, containerization, model registries, and the infrastructure of production ML.
Responsible AI and Fairness
Measuring and mitigating bias, ensuring transparency, and building ML systems that are accountable and equitable.