This module teaches you to monitor model performance in production and automate responses to drift. You'll configure drift detection, analyze metrics, and build automated retraining pipelines.

This module teaches you to implement systematic experiment tracking with MLflow. You'll log training runs, compare results across experiments, and select the best-performing model with documented rationale.

This module teaches you to systematically evaluate feature engineering techniques—including normalization, categorical encoding, and embeddings—using experiment tracking. You'll test multiple approaches, measure their impact, and document recommendations for the feature pipeline.

This module teaches you to process streaming data in real time using Azure Stream Analytics. You will write windowed aggregation queries, configure low-latency processing, and validate end-to-end latency meets requirements.

This module teaches you to evaluate and select messaging platforms for high-throughput streaming workloads. You will compare Kinesis, Kafka, and Event Hub across performance and cost dimensions, then produce an evidence-based recommendation.

This module teaches you to build end-to-end continuous integration and continuous delivery (CI/CD) pipelines for machine learning models using GitHub Actions. You will automate image building, registry push, and Kubernetes deployment triggered by code commits.

This module teaches you to troubleshoot continuous integration and continuous delivery (CI/CD) pipeline failures systematically. You will analyze logs, identify root causes, implement fixes, and track failure rate improvements over time.

This module teaches you to implement automated storage tiering for machine learning (ML) artifacts. You will configure lifecycle policies that move cold data to cheaper tiers, reducing costs while keeping the data accessible for infrequent needs.

This module teaches you to evaluate database options for a feature store. You will compare Azure Cosmos DB and Azure SQL Managed Instance (SQL MI) for low-latency feature serving, measuring performance and cost to produce a recommendation.

Apply your MLOps skills to design a complete model lifecycle pipeline from training through production monitoring. You'll integrate experiment tracking, CI/CD automation, drift detection, and storage optimization into a cohesive system with documented architecture and operational procedures.