AI learning path
Move models from notebooks into reproducible services with versioned data, automated tests, deployment controls, and production monitoring.
For ML engineers, platform teams, and production-minded developers
5 resources
Made With ML · Goku Mohandas
An end-to-end course covering product design, data, modeling, testing, reproducibility, serving, observability, and CI/CD for machine-learning systems.
DataTalks.Club
A project-led curriculum covering experiment tracking, orchestration, deployment, monitoring, testing, infrastructure, and a production capstone.
Full Stack Deep Learning
A systems-oriented course on infrastructure, experiment management, troubleshooting, testing, deployment, monitoring, data management, and team practices.
Google for Developers · Martin Zinkevich
A field guide to production ML that prioritizes solid pipelines, simple baselines, meaningful features, measurable objectives, and staged system evolution.
MLflow
Official quickstarts for experiment tracking, model packaging, evaluation, registry workflows, and serving.
