# Made With ML

Learn how to develop, deploy and iterate on production-grade ML applications.

Repository: https://github.com/GokuMohandas/Made-With-ML
Canonical: https://ross.abutalabs.com/products/made-with-ml
Homepage: https://madewithml.com
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: machine-learning, deep-learning, pytorch, natural-language-processing, data-science, python, mlops, data-engineering, data-quality, distributed-ml, llms, ray, distributed-training
Last push: 2026-03-04T23:44:21+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 70, release rhythm 41, longevity 100
- inputs: {"age_days": 2858, "days_push": 182, "days_rel": 182, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 49233, forks 7729 (observed 2026-08-28T04:12:11.575755+00:00)

## What it is
Made With ML is an open-source course teaching how to design, develop, deploy, and iterate on production-grade machine learning applications. It combines lessons on madewithml.com with hands-on Jupyter Notebook code covering MLOps, distributed training with Ray, and CI/CD workflows.

## Use cases
- learn mlops end to end
- deploy machine learning models to production
- learn distributed training with ray
- set up ci/cd for ml pipelines
- learn production ml best practices
- build an end-to-end ml system
- learn llm application development

## When to choose
- you want a structured, first-principles ML engineering course with runnable code
- you need to learn how to scale ML workloads in Python with Ray
- you want to practice going from notebook experimentation to production deployment

## When to avoid
- you need a production tool or library rather than educational material
- you want a framework-agnostic deep theory course without code
- you need non-Python ML tooling

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, llm-training, etl, ci-cd, testing
- domain: machine-learning, developer-tools, tutorials
- platform: python, cloud
- tags: mlops, ray, distributed-training, course, pytorch, production-ml, jupyter-notebooks, data-engineering, docker, kubernetes

## Member repositories
- GokuMohandas/Made-With-ML (main) score 66
- GokuMohandas/mlops-course (docs) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:11.575755+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:20:24.478837+00:00, confidence not recorded.
  - readme: https://github.com/GokuMohandas/Made-With-ML (fetched 2026-08-28T04:12:11.575755+00:00, sha cf45aebd01f2)
- Data as of 2026-08-30T08:39:29.467469+00:00.