Made With ML
Covers the part of machine learning that courses skip: testing, reproducibility, deployment, monitoring and the pipeline around a model. A model in a notebook is not a product, and this is the gap.
Course
AI & Machine Learning
Goku Mohandas
6 to 8 weeks
Advanced
Free
What it covers
Machine learning as software engineering. Project structure, data versioning, experiment tracking, testing models and data, packaging, serving, continuous integration, monitoring for drift, and the retraining loop.
Why this material is scarce
Courses end where the model works on the test set. Real projects begin there, and the failure modes afterwards are entirely different: data that shifts, a training pipeline nobody can reproduce, a model nobody knows is degrading. Very little free material treats this seriously.
Who it is for
People who already know how to train a model and now have to keep one running. If you are not there yet, do a fundamentals course first, because this assumes the modelling knowledge.
Best for: engineers responsible for machine learning in production rather than in a notebook.
Ready to start?
Opens on Goku Mohandas in a new tab.
Stuck on something specific?
Writing only gets you so far. If you want an answer to your situation rather than the general case, book a session and we will work through it together. Every session is free; a few slots open each week.
Follow along
New writing, resources and project ideas land here first.