Deploying a model is only the beginning. Keeping it accurate, cost-effective, and production-ready requires end-to-end MLOps workflows spanning training, monitoring, deployment, and storage. This course builds the skills to operationalize machine learning systems at enterprise scale.

You'll fine-tune vision models with Azure ML, track experiments via MLflow, and process streaming IoT telemetry using Azure Stream Analytics. You'll build CI/CD pipelines with GitHub Actions to automate model image builds for AKS, and optimize ML artifact storage using Azure Blob Storage lifecycle policies and feature store architecture. By the end of this course, you'll establish lifecycle standards with drift thresholds and retraining triggers, define experiment tracking practices, set latency targets for streaming data, own CI/CD pipeline reliability, and design storage tiering and feature store strategies balancing cost and performance. Designed for platform engineers adding MLOps expertise to their infrastructure skills. A basic understanding of machine learning concepts and experience with Python are expected.