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.
Infrastructures™ for Machine Learning Training/Inference in Production.
| Date | Stars |
|---|---|
| 2026-07-31 | 455 |
| 2026-08-02 | 455 |
| 2026-08-06 | 455 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
<p align="center"><img src="images/logo.png" width="100%"/></p> *** <p align="center"> :orange_book: List of real-world AI infrastructures (a.k.a., <strong>machine learning systems, pipelines, workflows</strong>, and <strong>platforms</strong>) for machine/deep learning training and/or inference <strong>in production</strong> :electric_plug:. This usually includes technology stack necessary to enable machine learning algorithms run in production environments in a stable, scalable and reliable way. The list is for my own learning purposes, but feel free to <strong>contribute</strong> / star / fork / pull request. Any recommendations and suggestions are welcome :tada:. </p> *** # Introduction This list contains some popular actively-maintained AI infrastructures that focus on one or more of the following topics: - Architecture of **end-to-end** machine learning training **pipelines**. - **Inference** at scale in production on Cloud :cloud: or on end devices :iphone:. - **Compiler and optimization** stacks for deployments on variety of devices. - Novel ideas of efficient large-scale **distributed training**. in **no specific order**. This list cares more about overall architectures of AI solutions in production instead of individual machine/deep learning training or inference frameworks. My learning goals are: understand the workflows and principles of how to build (large-scale) systems that can enable machine learning in production. # Platforms ### [TFX](https://www.tensorflow.org/tfx/) - TensorFlow Extended ([Google](https://www.google.com/about/)) > TensorFlow Extended (TFX) is a [TensorFlow](https://www.tensorflow.org/)-based general-purpose machine learning platform implemented at Google. | [__homepage__](https://www.tensorflow.org/tfx/) | [__talk__](https://www.youtube.com/watch?v=vdG7uKQ2eKk) | [__paper__](https://dl.acm.org/citation.cfm?id=3098021) | #### Architecture: <p align="center"><img src="images/google-tfx-arch.png" width="90%"/></p> #### Components: - **TensorFlow Data Validation**: a library for exploring and validating machine learning data. - **TensorFlow Transformation**: perform full-pass analyze phases over data to create transformation graphs that are consistently applied during training and serving. - **TensorFlow Model Analysis**: libraries and visualization components to compute full-pass and sliced model metrics over large datasets, and analyze them in a notebook. - **TensorFlow Serving**: a flexible, high-performance serving system for machine learning models, designed for production environments ### [Kubeflow](https://www.kubeflow.org/) - The Machine Learning Toolkit for [Kubernetes](https://kubernetes.io/) ([Google](https://www.google.com/about/)) > The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on [Kubernetes](https://kubernetes.io/) simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running [Kubernetes](https://kubernetes.io/), you should be able to run Kubeflow. > Kubeflow started as an open sourcing of the way Google ran [TensorFlow](https://www.tensorflow.org/) internally, based on a pipeline called TensorFlow Extended. | [__homepage__](https://www.kubeflow.org/) | [__github__](https://github.com/kubeflow/kubeflow) | [__documentation__](https://www.kubeflow.org/docs/about/kubeflow/) | [__blog__](https://kubernetes.io/blog/2017/12/introducing-kubeflow-composable/) | [__talk__](https://conferences.oreilly.com/strata/strata-ny-2018/public/schedule/detail/69041) | [__slices__](https://cdn.oreillystatic.com/en/assets/1/event/278/Kubeflow%20explained_%20Portable%20machine%20learning%20on%20Kubernetes%20Presentation.pdf) | #### Components: - **Notebooks**: a JupyterHub to create and manage interactive Jupyter notebooks. - **TensorFlow Model Training**: a TensorFlow Training Contr
Excerpt of 52,658 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2f6346d38557c4ae, topic:quantization, topic:model-compression, topic:pruning