Building a recommendation model is only part of the challenge. Keeping it accurate, fast, and reliable in production requires a different set of skills entirely. This course teaches you to deploy, monitor, and maintain high-scale recommendation engines using Azure's MLOps toolchain.

You will configure low-latency prediction endpoints with Azure Cache for Redis for real-time feature hydration. You will architect automated CI/CD and retraining pipelines using Azure ML SDK v2, implementing canary and blue-green strategies for zero-downtime swaps. You will also build statistical drift monitoring using the Population Stability Index (PSI) and Wasserstein Distance over tensors, aligning telemetry with long-term utility indicators like Saves, Dwell Time, and Shares. Designed for DevOps Engineers, MLOps Specialists, and Cloud Architects establishing automated lifecycles, monitoring, and sub-10ms serving for distributed ML workloads. You should be comfortable with Python, Azure ML, and core ML metrics.