The discipline of actually running machine learning in production.
5 min read · Skill IT Education
Training an accurate model in a notebook is one thing. Reliably deploying, monitoring, and updating it in production — where real users depend on it — is a different, ongoing challenge.
Versioning models and data, automating retraining pipelines, monitoring for performance drift, and managing the infrastructure models run on.
As more organizations move from experimenting with ML to depending on it operationally, the gap between data science and production engineering has become its own career path.
This article is the short version. The full program covers it hands-on, in labs, with a mentor reviewing your work.
See Machine Learning Track