MLOps (Machine Learning Operations) for Business

MLOps (Machine Learning Operations) for Business
Published 8/2026
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch

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.
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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