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AI · alternatives · 2026

24 alternatives to Machine Learning into Production Workshop: MLFlow, Airflow Tutorials

Contribute to whiteboxml/teaching-ml-in-production development by creating an account on GitHub.

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Machine Learning into Production Workshop: MLFlow, Airflow Tutorials launched in 2019; newer entries below may have overtaken it.

  1. 1

    Simple and comprehensive tutorials in TensorFlow. Contribute to easy-tensorflow/easy-tensorflow development by creating an account on GitHub.

    2018 · github.com · its alternatives →

  2. 2

    Open-source machine learning library by Google

    2017 · tensorflow.org · its alternatives →

  3. 3

    A curated collection of machine learning projects

    2017 · its alternatives →

  4. 4

    https://the-pocket.github.io/Tutorial-Codebase-Knowledge/

    2025 · github.com · its alternatives →

  5. 5

    a delightful machine learning tool that allows you to train, test, and use models without writing code - GitHub - nidhaloff/igel at v0.4.0

    2021 · github.com · its alternatives →

  6. 6

    a delightful machine learning tool that allows you to train, test, and use models without writing code - nidhaloff/igel

    2020 · github.com · its alternatives →

  7. 7
    ZenML▲84

    Create reproducible machine learning pipelines

    2020 · github.com · its alternatives →

  8. 8

    :satellite: Simple and ready-to-use tutorials for TensorFlow - GitHub - instillai/TensorFlow-Course: :satellite: Simple and ready-to-use tutorials for TensorFlow

    2018 · github.com · its alternatives →

  9. 9
    ML5.js▲249

    Friendly machine learning for the web

    2018 · ml5js.org · its alternatives →

  10. 10

    [STILL ACTIVE] Learn the GitHub workflow by contributing code in a fun simulation project - danthareja/contribute-to-open-source

    2017 · github.com · its alternatives →

  11. 11

    Breathtaking visuals for learning ML techniques.

    2017 · ml-playground.com · its alternatives →

  12. 12

    :satellite: Organized & Useful Resources about Deep Learning with TensorFlow - astorfi/TensorFlow-World-Resources

    2018 · github.com · its alternatives →

  13. 13

    Open-source machine learning library by Google

    2018 · tensorflow.org · its alternatives →

  14. 14

    My Machine Learning Web Service. Contribute to pplonski/my_ml_service development by creating an account on GitHub.

    2020 · github.com · its alternatives →

  15. 15

    Machine Learning Experiments based on Git

    2021 · studio.datachain.ai · its alternatives →

  16. 16GF

    2018 · dataturks.com · its alternatives →

  17. 17

    Open-source, easily create ready-to-use ML models for NLP

    2022 · github.com · its alternatives →

  18. 18
    GoLearn▲81

    Machine Learning for Go. Contribute to sjwhitworth/golearn development by creating an account on GitHub.

    2014 · github.com · its alternatives →

  19. 19

    GitHub for TensorFlow machine learning models

    2018 · tensorflow.org · its alternatives →

  20. 20
    UnionML▲77

    The easiest way to build and deploy ML microservices

    2022 · union.ai · its alternatives →

  21. 21

    We built a white-labeled workflow builder that platforms can embed to offer automation capabilities to their users: - JavaScript code action sandboxing - Native UI experience - Custom action support - Simple integration We're bootstrapped and focused on making workflow automation accessible to more platforms. Demo: https://youtu.be/wVspgI27x9A Product Hunt launch: https://www.producthunt.com/posts/embed-workflow

    2023 · embedworkflow.com · its alternatives →

  22. 22

    Run, deploy & scale state of the art machine learning tech

    2017 · machinebox.io · its alternatives →

  23. 23

    We are the developers of an open-source package Metaflow that we started at Netflix. Metaflow provides a human-friendly interface to the full stack of ML infrastructure, including data access, compute, workflow orchestration, and versioning. It is used by hundreds of companies across industries. Over the past years, we have seen that there are two major stumbling blocks for folks who want to learn to build real-world ML applications: 1) Setting up the full infrastructure stack in the cloud costs time and money. The investment is worth it once you know what you want to do, but that's not…

    2023 · outerbounds.com · its alternatives →

  24. 24

    Create ML playgrounds for building machine learning models. For developers and data scientists. - GitHub - krishkumar/createml-playgrounds: Create ML playgrounds for building machine learning models. For developers and data scientists.

    2018 · github.com · its alternatives →

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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →