GokuMohandas/Made-With-ML
Learn how to develop, deploy and iterate on production-grade ML applications.
From the publisher
README & documentation
Source previewRead the project’s overview, installation instructions and usage examples. The original README is the source of truth.
Made With ML Design · Develop · Deploy · Iterate Join 40K+ developers in learning how to responsibly deliver value with ML. Learn how to combine machine learning with software engineering to design, develop, deploy and iterate on production-grade ML applications. In this course, we'll go from experimentation (design + development) to production (deployment + iteration). We'll do this iteratively by motivating the components that will enable us to build a reliable production system. Be sure to watch the video below for a quick overview of what we'll be building. Machine learning is not a separate industry, instead, it's a powerful way of thinking about data that's not reserved for any one type of person. Be sure to go through the course for a much more detailed walkthrough of the content on this repository. We will have instructions for both local laptop and Anyscale clusters for the sections below,…
Read the full README ↗ · Preview checked 2026-10-31T04:54:09.040Z
Inside the original README — Document outline
- Lessons
- Overview
- Audience
- Set up
- Cluster
- Git setup
- Credentials
- Virtual environment
- Notebook
- Scripts
- Training
- Tuning
Headings are captured from the source. Links open the publisher’s document, not a locally hosted copy.
Links from the README
References supplied by the publisher, not independently verified endorsements. Check the destination before downloading files or entering credentials.
Documentation belongs to its respective authors. Reported project/model license: MIT. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
What this repository does
Learn how to develop, deploy and iterate on production-grade ML applications.
Repository facts
- Owner
- GokuMohandas
- Primary language
- Jupyter Notebook
- Stars
- 49,857
- Forks
- 7,813
- Open issues + pull requests
- 25
- License
- MIT
- Archived
- No
- Default branch
- main
- Created
- 2018-11-05T03:44:27.000Z
- Last push
- 2026-03-04T23:44:21.000Z
Topics and intended use
Owner-supplied topics: data-engineering, data-quality, data-science, deep-learning, distributed-ml, distributed-training, llmops, llms, machine-learning, mlops, natural-language-processing, python, pytorch, ray
Review the README for scope, installation, examples and limitations. We do not run repository code or certify it.
Evaluate before installing
Review licensing and dependencies, inspect recent commits and unresolved issues, and test in an isolated environment before production use. Stars and forks alone cannot answer these questions.
Source and freshness
Source: GitHub. Metadata observed 2026-10-31T12:19:22.904Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.