GokuMohandas/Made-With-ML
Learn how to develop, deploy and iterate on production-grade ML applications.
Covers MLOps workflows with Ray for distributed training and tuning, integrated experiment tracking and model testing, and CI/CD pipelines for continuous model deployment. Uses first-principles teaching combined with production-grade software engineering practices—including data validation, monitoring, and versioning—to build end-to-end ML systems that scale from laptop to cloud clusters (Ray, Anyscale, Kubernetes, AWS/GCP).
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Last pushed
Mar 04, 2026
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