Machine Learning in Production

The whole lifecycle, not just the model

Much machine learning teaching focuses on the model itself. This course starts earlier, with scoping, and follows the work through data, modeling and deployment. That sequence matches the points where production projects often run into trouble, so it works as a map of the job rather than a single technique.

What it helps you think through

• Whether a problem is scoped tightly enough to build against.
• How prototype development differs from a system that has to run reliably.
• What continuous improvement looks like once a model is live.

Who should take it

It suits engineers and data practitioners who have built models in notebooks and now need to get them into a working product. Managers who sign off on ML projects will also get a clearer view of where the time goes.