Top AI Repos โ open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
๐ ๐ง๐ต๐ฒ ๐๐๐น๐น ๐ฆ๐๐ฎ๐ฐ๐ธ ๐ณ-๐ฆ๐๐ฒ๐ฝ๐ ๐ ๐๐ข๐ฝ๐ ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ | ๐๐ฒ๐ฎ๐ฟ๐ป ๐ ๐๐ & ๐ ๐๐ข๐ฝ๐ for free by designing, building and deploying an end-to-end ML batch system ~ ๐ด๐ฐ๐ถ๐ณ๐ค๐ฆ ๐ค๐ฐ๐ฅ๐ฆ + 2.5 ๐ฉ๐ฐ๐ถ๐ณ๐ด ๐ฐ๐ง ๐ณ๐ฆ๐ข๐ฅ๐ช๐ฏ๐จ & ๐ท๐ช๐ฅ๐ฆ๐ฐ ๐ฎ๐ข๐ต๐ฆ๐ณ๐ช๐ข๐ญ๐ด
| Date | Stars |
|---|---|
| 2026-07-30 | 980 |
| 2026-08-17 | 982 |
| 2026-08-18 | 983 |
| 2026-08-20 | 984 |
| 2026-08-24 | 985 |
| 2026-08-25 | 984 |
| 2026-08-28 | 985 |
| 2026-08-30 | 985 |
| 2026-09-01 | 985 |
| 2026-09-02 | 986 |
| 2026-09-03 | 987 |
| 2026-09-07 | 987 |
| 2026-10-05 | 986 |
| 2026-10-15 | 987 |
| 2026-10-25 | 987 |
| 2026-10-26 | 987 |
| 2026-10-30 | 988 |
| 2026-10-31 | 988 |
| 2026-11-04 | 988 |
Today
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growth rate 0.10%/day
# The Full Stack 7-Steps MLOps Framework
`Learn MLE & MLOps for free by designing, building, deploying and monitoring an end-to-end ML batch system | source code + 2.5 hours of reading & video materials on Medium`
This repository contains a **7-lesson FREE course** to teach you how to **build a production-ready ML batch system**. Its primary focus is to engineer a scalable system using MLOps good practices. You will implement an ML system for forecasting hourly energy consumption levels across Denmark.
You will **learn how to build, train, serve, and monitor an ML system** using a batch architecture. We will show you how to integrate an experiment tracker, a model registry, a feature store, Docker, Airflow, GitHub Actions and more!
**Level:** Intermediate to Advanced | This **course targets** MLEs who want to build end-to-end ML systems and SWEs who wish to transition to MLE.
------
Following the **documentation on GitHub** and the [lessons on Medium](#lessons), you have *2.5 hours of reading & video materials*, which will help you understand every piece of the code!
**At the end of the course, you will know how to build everything from the diagram below ๐**
Don't worry if something doesn't make sense to you. We will explain everything in detail in the [Medium lessons](#lessons).
If you are unsure if this course is for you, [here is an article presenting a high-level overview](https://pub.towardsai.net/the-full-stack-7-steps-mlops-framework-6599a0c6e295) of all the components you will build during the series.
<p align="center">
<img src="images/architecture.png">
</p>
<div align="center">
<a href="https://youtu.be/OKk9U310qYE">
<strong>Check out this short video to see what you will build during the course ๐</strong>
<br/>
<br/>
<img src="images/screenshot_introduction_video.png" alt="Introduction Video" style="width:75%;">
</a>
</div>
<br/>
<br/>
*You can safely use this code as you like, as long as you respect the terms and agreement of the MIT License.*
``<<< Using all the tools suggested in the course will be free of charge, except the ones from Lesson 7, where you will be deploying your application to GCP which will cost you ~20$. >>>``
# Table of Contents
1. [What You Will Learn](#learn)
2. [Lessons & Tutorials](#lessons)
3. [Costs](#costs)
4. [Ask Questions](#ask-questions)
5. [Data](#data)
6. [Code Structure](#structure)
7. [Set Up Additional Tools](#tools)
8. [Usage](#usage)
9. [Installation & Usage for Development](#installation)
10. [Licensing & Contributing](#licensing)
11. [Support](#support)
--------
# ๐ค 1. What You Will Learn <a name=learn></a>
**At the end of this 7 lessons course, you will know how to:**
* design a batch-serving architecture
* use Hopsworks as a feature store
* design a feature engineering pipeline that reads data from an API
* build a training pipeline with hyper-parameter tunning
* use W&B as an ML Platform to track your experiments, models, and metadata
* implement a batch prediction pipeline
* use Poetry to build your own Python packages
* deploy your own private PyPi server
* orchestrate everything with Airflow
* use the predictions to code a web app using FastAPI and Streamlit
* use Docker to containerize your code
* use Great Expectations to ensure data validation and integrity
* monitor the performance of the predictions over time
* deploy everything to GCP
* build a CI/CD pipeline using GitHub Actions
If that sounds like a lot, don't worry. After you cover this course, you will understand everything we said before. Most importantly, you will know WHY we used all these tools and how they work together as a system.
# ๐ค 2. Lessons & Tutorials <a name=lessons></a>
The course consists of 7 lessons hosted on Medium Towards Data Science publication. We also provide a bonus lesson where we openly discuss potential improvements that could be made to the current architecture and trade-offs we had to take during the course. The course adds up to *2.5Excerpt of 31,735 characters
Read on GitHub
Paul Iusztin ยท Decoding AI
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Would you bet a product on this? Bounded 0โ100 and slow moving.
matched fp:acfa2f70297480c1, topic:mlops, topic:model-registry, topic:feature-store