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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 — no stories — 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.

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


We are also on:

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🖋️ Medium

🐦 Twitter(X)

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  1. articles-code articles-code Public

    💻 Decoding ML articles hub: Hands-on articles with code on production-grade ML

    Jupyter Notebook 51 8

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

    🤖 𝗟𝗲𝗮𝗿𝗻 for 𝗳𝗿𝗲𝗲 how to 𝗯𝘂𝗶𝗹𝗱 an end-to-end 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗟𝗟𝗠𝗢𝗽𝘀 best practices: ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 11 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘭𝘦𝘴𝘴𝘰𝘯𝘴

    Python 630 130

Repositories

Showing 3 of 3 repositories
  • llm-twin-course Public

    🤖 𝗟𝗲𝗮𝗿𝗻 for 𝗳𝗿𝗲𝗲 how to 𝗯𝘂𝗶𝗹𝗱 an end-to-end 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗟𝗟𝗠𝗢𝗽𝘀 best practices: ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 11 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘭𝘦𝘴𝘴𝘰𝘯𝘴

    Python 630 MIT 130 1 1 Updated May 16, 2024
  • articles-code Public

    💻 Decoding ML articles hub: Hands-on articles with code on production-grade ML

    Jupyter Notebook 51 MIT 8 0 0 Updated May 1, 2024
  • .github Public
    0 0 0 0 Updated Mar 20, 2024

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