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@decodingml

Decoding ML

Battle-tested content on designing, coding, and deploying production-grade ML & MLOps systems.

The hub for continuous learning on production-grade ML & MLOps systems


Welcome to Decoding ML

When ML production systems look encoded - we'll help you decode it.


Decoding ML is a publication that creates battle-tested content on building production-grade ML systems leveraging good SWE and MLOps practices.

Our motto is "More engineering, less F1 scores."

Following Decoding ML, you will learn about the entire lifecycle of an ML system, from system design to deploying and monitoring.

Decoding ML is the hub for continuous learning on:

  • ML system design
  • ML engineering
  • MLOps
  • Large language models
  • Computer vision

We are all about end-to-end ML use cases that can directly be applied in the real world โ€” no stories โ€” just hands-on content.

Why follow?

Join Decoding ML for battle-tested content on designing, coding, and deploying production-grade ML & MLOps systems. Every week. For FREE.

No more bedtime stories in Jupyter Notebooks.

โ†’ DML is all about hands-on advice from our 10+ years of experience in AI.


We are also on:

โœ‰๏ธ Newsletter

๐Ÿ–‹๏ธ Medium

๐Ÿฆ Twitter(X)

๐Ÿ“ฑ LinkedIn

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  1. articles-code articles-code Public

    ๐Ÿ’ป Decoding ML articles hub: Hands-on articles with code on production-grade ML

    Jupyter Notebook 112 17

  2. llm-twin-course llm-twin-course Public

    ๐Ÿค– ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป for ๐—ณ๐—ฟ๐—ฒ๐—ฒ how to ๐—ฏ๐˜‚๐—ถ๐—น๐—ฑ an end-to-end ๐—ฝ๐—ฟ๐—ผ๐—ฑ๐˜‚๐—ฐ๐˜๐—ถ๐—ผ๐—ป-๐—ฟ๐—ฒ๐—ฎ๐—ฑ๐˜† ๐—Ÿ๐—Ÿ๐—  & ๐—ฅ๐—”๐—š ๐˜€๐˜†๐˜€๐˜๐—ฒ๐—บ using ๐—Ÿ๐—Ÿ๐— ๐—ข๐—ฝ๐˜€ best practices: ~ ๐˜ด๐˜ฐ๐˜ถ๐˜ณ๐˜ค๐˜ฆ ๐˜ค๐˜ฐ๐˜ฅ๐˜ฆ + 12 ๐˜ฉ๐˜ข๐˜ฏ๐˜ฅ๐˜ด-๐˜ฐ๐˜ฏ ๐˜ญ๐˜ฆ๐˜ด๐˜ด๐˜ฐ๐˜ฏ๐˜ด

    Python 3k 478

  3. hands-on-personalized-recommender hands-on-personalized-recommender Public

    Jupyter Notebook 8 4

Repositories

Showing 4 of 4 repositories
  • llm-twin-course Public

    ๐Ÿค– ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป for ๐—ณ๐—ฟ๐—ฒ๐—ฒ how to ๐—ฏ๐˜‚๐—ถ๐—น๐—ฑ an end-to-end ๐—ฝ๐—ฟ๐—ผ๐—ฑ๐˜‚๐—ฐ๐˜๐—ถ๐—ผ๐—ป-๐—ฟ๐—ฒ๐—ฎ๐—ฑ๐˜† ๐—Ÿ๐—Ÿ๐—  & ๐—ฅ๐—”๐—š ๐˜€๐˜†๐˜€๐˜๐—ฒ๐—บ using ๐—Ÿ๐—Ÿ๐— ๐—ข๐—ฝ๐˜€ best practices: ~ ๐˜ด๐˜ฐ๐˜ถ๐˜ณ๐˜ค๐˜ฆ ๐˜ค๐˜ฐ๐˜ฅ๐˜ฆ + 12 ๐˜ฉ๐˜ข๐˜ฏ๐˜ฅ๐˜ด-๐˜ฐ๐˜ฏ ๐˜ญ๐˜ฆ๐˜ด๐˜ด๐˜ฐ๐˜ฏ๐˜ด

    decodingml/llm-twin-courseโ€™s past year of commit activity
    Python 2,970 MIT 478 10 0 Updated Nov 28, 2024
  • decodingml/hands-on-personalized-recommenderโ€™s past year of commit activity
    Jupyter Notebook 8 Apache-2.0 4 0 1 Updated Nov 28, 2024
  • articles-code Public

    ๐Ÿ’ป Decoding ML articles hub: Hands-on articles with code on production-grade ML

    decodingml/articles-codeโ€™s past year of commit activity
    Jupyter Notebook 112 MIT 17 0 0 Updated Oct 17, 2024
  • .github Public
    decodingml/.githubโ€™s past year of commit activity
    0 0 0 0 Updated Mar 20, 2024

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