#1010th of 10 in Learning and cookbooks

made-with-ml

MLOps course with code for training, tuning, and serving a text classifier

Documentation ↗ Website ↗ Repository on GitHub ↗

Stars: 49.7k
License: MIT
Last commit: Mar 2026
Last release: Mar 2026
Language: Jupyter Notebook

Stars, last 29 weeks+3,183

46,549 on 24 Mar 202649,732 on 13 Oct 2026

From GitHub's daily star counts.

Checked 14 Oct 2026 on GitHub.

Overview

A course repository that walks through building a text classifier end to end: a Jupyter notebook, then refactored Python scripts for training, tuning, evaluation, inference and serving. It uses Ray for distributed workloads, MLflow for experiment tracking, pytest for code, data and model tests, and Anyscale Jobs and Services for production deployment.

Who it is for: Developers and data scientists learning to take ML from notebook to production

Strengths

Covers the full path: notebook, scripts, tests, MLflow tracking, Ray Serve, CI/CD
Runs on a laptop without a GPU, with configurable worker counts
Separate pytest suites for code, data and trained models
Plain Python scripts with documented CLI arguments for each workload

Weaknesses

Production section depends on Anyscale (account, CLI token, S3 upload paths)
Example task is a small text classifier, not an LLM application
Setup is built around Ray; other stacks need adaptation
README promotes a paid live cohort

What it needs

GPU optional
Needs Ray, MLflow, Anyscale, AWS S3, pytest, pre-commit
port 8000

Also in Learning and cookbooks

See all 10

Also in Learning and cookbooks
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1	generative-ai-for-beginners21-lesson course on building generative AI apps in Python and TypeScript121.3k starsMIT	80 out of 100
2	ai-engineering-from-scratchHands-on curriculum for building AI systems from math to agents68.8k starsMIT	79 out of 100
3	llms-from-scratchCode for the book on building a GPT-style LLM in PyTorch106.5k starscustom license	75 out of 100
#4	hello-agentsChinese-language course on building LLM agents from scratch83k starscustom license	74 out of 100
#5	ai-agents-for-beginners18-lesson course on building AI agents with Microsoft Agent Framework77.1k starsMIT	72 out of 100

How to choose

Check the date: guides older than a year often use APIs that changed.
Prefer material with runnable notebooks or repos over slides.
Pick by level: curricula start from zero, cookbooks assume you already build.

Health

Releases: 1 in the last 12 monthsLast one 224 days ago
Top contributor: No commits by peopleIn the last 12 months
Security advisories: NoneOn GitHub, in the last 12 months
OpenSSF Scorecard: Not checked

Read from GitHub, deps.dev and OpenSSF on 14 Oct 2026. Bots and coding-agent accounts are left out of the commit counts. Not part of the score: how we check.

License in plain words

MIT Permissive

Use it at work: Yes
Change it and rebrand it: Yes
Sell it or host it for others: Yes

Your part: Keep the copyright and license notice in copies you ship.

A plain summary, not legal advice. Read the license on GitHub ↗ (opens in a new tab)

#10 of 10 in Learning and cookbooks on Archestack, as of 14 Oct 2026.

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