Machine Learning Engineering for Production (MLOps) Specialization

DeepLearning.AI concentrates on the unglamorous half most ML courses skip: building pipelines, deploying models, versioning data, and watching for the drift that quietly rots a model's accuracy after launch. It's the gap between a model that impresses in a notebook and one that survives real users.

Two things shape who it suits. It assumes you already train models, so it's no place to start learning ML, and it leans on the TensorFlow and TFX ecosystem, so you'll translate some specifics if your team runs a different stack.

You can audit it free on Coursera; the certificate runs about $49 a month, with financial aid available. The practices it teaches only grow more relevant as more teams try to run models at scale (as of 2026).

The MLOps Specialization bridges the gap between training models and running them in production. Across four courses, you design ML pipelines, manage data and model versioning, deploy with TFX, and monitor for data and concept drift.