This flagship course walks working engineers through the full lifecycle of production machine learning: framing problems, building data pipelines, training and evaluating models, and packaging them as services. You will then learn deployment patterns, MLOps automation, drift monitoring, and the scaling and cost-management practices that keep ML systems reliable and affordable.
About this courseThis is a self-paced educational course. Completing it does not award a professional certification, license, degree or academic credit, and it does not count toward any licensing or continuing-education requirement. Exam-prep courses are independent study material: we are not affiliated with or endorsed by the exam provider, no exam voucher is included, and a certification is only awarded by the provider after you pass its exam. It makes no promise of employment, pay or exam results.