Top AI Repos — 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.
Learn how to design, develop, deploy and iterate on production-grade ML applications.
| Date | Stars |
|---|---|
| 2026-07-24 | 3387 |
| 2026-07-25 | 3387 |
| 2026-07-28 | 3387 |
| 2026-07-30 | 3387 |
| 2026-08-06 | 3387 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# MLOps Course Learn how to combine machine learning with software engineering to design, develop, deploy and iterate on production-grade ML applications. - Lessons: https://madewithml.com/ - Code: [GokuMohandas/Made-With-ML](https://github.com/GokuMohandas/Made-With-ML) <a href="https://madewithml.com/#course"> <img src="https://madewithml.com/static/images/lessons.png" alt="lessons"> </a> ## Overview In this course, we'll go from experimentation (model design + development) to production (model deployment + iteration). We'll do this iteratively by motivating the components that will enable us to build a *reliable* production system. <blockquote> <img width=20 src="https://upload.wikimedia.org/wikipedia/commons/thumb/0/09/YouTube_full-color_icon_%282017%29.svg/640px-YouTube_full-color_icon_%282017%29.svg.png"> Be sure to watch the video below for a quick overview of what we'll be building. </blockquote> <div align="center"> <a href="https://youtu.be/AWgkt8H8yVo"><img src="https://img.youtube.com/vi/AWgkt8H8yVo/0.jpg" alt="Course overview video"></a> </div> <br> - **💡 First principles**: before we jump straight into the code, we develop a first principles understanding for every machine learning concept. - **💻 Best practices**: implement software engineering best practices as we develop and deploy our machine learning models. - **📈 Scale**: easily scale ML workloads (data, train, tune, serve) in Python without having to learn completely new languages. - **⚙️ MLOps**: connect MLOps components (tracking, testing, serving, orchestration, etc.) as we build an end-to-end machine learning system. - **🚀 Dev to Prod**: learn how to quickly and reliably go from development to production without any changes to our code or infra management. - **🐙 CI/CD**: learn how to create mature CI/CD workflows to continuously train and deploy better models in a modular way that integrates with any stack. ## Audience Machine learning is not a separate industry, instead, it's a powerful way of thinking about data that's not reserved for any one type of person. - **👩💻 All developers**: whether software/infra engineer or data scientist, ML is increasingly becoming a key part of the products that you'll be developing. - **👩🎓 College graduates**: learn the practical skills required for industry and bridge gap between the university curriculum and what industry expects. - **👩💼 Product/Leadership**: who want to develop a technical foundation so that they can build amazing (and reliable) products powered by machine learning. ## Set up Be sure to go through the [course](https://madewithml/#course) for a much more detailed walkthrough of the content on this repository. We will have instructions for both local laptop and Anyscale clusters for the sections below, so be sure to toggle the ► dropdown based on what you're using (Anyscale instructions will be toggled on by default). If you do want to run this course with Anyscale, where we'll provide the **structure**, **compute (GPUs)** and **community** to learn everything in one weekend, join our next upcoming live cohort → [sign up here](https://4190urw86oh.typeform.com/madewithml)! ### Cluster We'll start by setting up our cluster with the environment and compute configurations. <details> <summary>Local</summary><br> Your personal laptop (single machine) will act as the cluster, where one CPU will be the head node and some of the remaining CPU will be the worker nodes. All of the code in this course will work in any personal laptop though it will be slower than executing the same workloads on a larger cluster. </details> <details open> <summary>Anyscale</summary><br> We can create an [Anyscale Workspace](https://docs.anyscale.com/develop/workspaces/get-started) using the [webpage UI](https://console.anyscale.com/o/madewithml/workspaces/add/blank). ```md - Workspace name: `madewithml` - Project: `madewithml` - Cluster environment name: `madewithml-cluster-env`
Excerpt of 22,771 characters
Read on GitHub
Goku Mohandas
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7115983b628435c3, topic:deep-learning, topic:pytorch
matched fp:7115983b628435c3, topic:mlops, name:mlops, readme:mlops
matched fp:7115983b628435c3, topic:data-engineering
matched fp:7115983b628435c3, topic:ray