A machine learning model that works in a notebook is not a model your organisation can rely on. The gap between a local prototype and a governed, scalable production system is where most Machine Learning projects stall, and closing it takes engineering skills that data science training rarely covers.
This advanced course closes that gap on Azure. You begin by rebuilding monolithic code into reusable, component-based Azure Machine Learning pipelines with the SDK v2, automating ingestion, feature engineering, and training. You then run automated hyperparameter optimisation with HyperDrive, using Bayesian sampling and early-termination policies to find the best model without wasting compute.
Next you take that model into production, packaging it into a containerised managed online endpoint with auto-scaling and load-balancing to hold availability under load. Finally you add accountability: the Responsible AI Dashboard and SHAP for interpretability and fairness, and Application Insights for continuous drift monitoring that triggers retraining before accuracy decays.
This course is for technical individual contributors, including data analysts, software engineers, and developers, who already understand Machine Learning concepts and want to operate models in production. You finish with a complete end-to-end MLOps portfolio asset, built across four scaffolded capstone phases.
