MLOps & Responsible AI

Reliable, fair AI in production. Deployment, monitoring, drift, retraining, fairness, privacy and governance — plus healthcare AI as a case study.

• MLOps, Deployment & Responsible AI — Running AI reliably in the real world. Learn deployment patterns (batch, real-time API, streaming, edge), model packaging and serving (Docker, ONNX, model servers), latency and throughput SLOs, CI/CD for ML, experiment tracking and model registries, feature stores, monitoring (data drift with PSI, concept drift, performance decay), retraining strategies, A/B and shadow testing, fairness metrics (demographic parity, equal opportunity), explainability, privacy, security and AI governance — with Python computing PSI drift, disparate impact and p95 latency.
• Healthcare Data Science & Medical AI — Turning medical data into decisions. Learn medical data types (signals, images, EHR, genomics), diagnostic test metrics (sensitivity, specificity, PPV, NPV, ROC/AUC) and the effect of prevalence, machine-learning basics (logistic regression, trees, CNNs), training/validation/test splits and leakage, bias and fairness, explainability, clinical validation and regulation of AI as a medical device — with Python computing test metrics and training a logistic-regression model.