A comprehensive curriculum for designing, developing, and deploying production-grade machine learning applications. It integrates data engineering, model versioning, and CI/CD pipelines into a unified Python-based workflow.
Tech Stack
Jupyter NotebookEducation & Learning
Why Made-With-ML?
• End-to-end lifecycle coverage
• Production-first architecture focus
• Regularly updated community content
Limitations
• No formal certification
• Requires self-discipline
• High prerequisite knowledge
3/5/2026
Last Update
7,307
Forks
24
Issues
MIT
License
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