3 free resources on this topic. Everything here is free and hand-picked. You can also search within this topic.
Hands-on tutorial (Sept. 2026) showing how to spot when the text data a live AI system sees starts to shift, using a domain classifier and centroid distance.
Why I recommend it: Free tutorial. Assumes you know Python and basic machine learning.
A free four-month, hands-on machine learning engineering course covering Python, regression and classification, XGBoost, deep learning with TensorFlow, Docker, Kubernetes and cloud deployment, with homework, projects and a Slack community. The 2026 cohort started September 14, 2026, and you can still start now.
Why I recommend it: One of the strongest free routes into machine learning work, because you finish with deployed projects, not just notes.
A Duke Coursera course covering the Python tooling MLOps roles rely on — virtual environments, package management, linting, testing, and deploying models as reproducible pipelines.
Why I recommend it: Useful if you are targeting ML engineering or data-science roles and need to show you can ship models, not just train them.
