This course contains the use of artificial intelligence.
Most companies can build a machine learning model. Very few can keep it working. A model that scored 94% accuracy in the lab can quietly degrade to 61% within six months of going live, and nobody notices until a customer complains, a regulator asks a question, or a forecast costs the business real money. This course closes that gap. You'll learn MLOps — Machine Learning Operations — the discipline that treats a deployed model less like a finished product and more like a living system that needs monitoring, maintenance, and governance.
We start with why models "drift": the data the world sends your model tomorrow is never quite the data it trained on yesterday. Customer behavior shifts, markets move, seasons change, and your model's assumptions age out from under it. You'll learn to recognize the early warning signs, measure drift with the right metrics, and build the monitoring dashboards that catch problems before your customers do.
From there we move into the operational backbone: deployment strategies that don't crash production, retraining pipelines that update models safely, version control for models (not just code), and the CI/CD practices that make ML releases boring instead of terrifying. We close with governance — the guardrails that keep AI systems compliant, auditable, and defensible when a regulator or a board member asks "how do you know this model is still right?"
This is not a coding bootcamp. It's a business-focused course built for people who need to manage, fund, or oversee AI systems without necessarily writing the training code themselves — though technical learners will find plenty of depth here too. By the end, you'll be able to speak the language of data scientists and engineers, ask the right questions in a model review, and build the operational muscle that turns a one-time AI win into a durable competitive advantage.

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