20 Open Source Tools I Recommend to Build, Share, and Run AI Projects

• datasets

  17 19,333 9.5 Python

  🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools

  

  Datasets library repository for accessing and sharing datasets with the community.

• SaaSHub

  www.saashub.com featured

  SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

  

• examples

  158 7,963 5.6 Jupyter Notebook

  TensorFlow examples (by tensorflow)

  

  TensorFlow is an end-to-end ML platform for creating, running, training, and deploying ML models to production. Its focus is on deep neural networks.

• modelzoo

  3 971 5.0 Python

  

  Check out the Cerebras Model Zoo repository to look at their models.

• gradio

  126 34,358 9.9 Python

  Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!

  

  Like Streamlit, Gradio is an open source tool for sharing your ML models as web apps with the public. This tool creates an interactive interface for your model. This customizable interface supports integration with popular frameworks like PyTorch and Scikit-learn.

• streamlit

  296 35,957 9.9 Python

  Streamlit — A faster way to build and share data apps.

  

  Streamlit is an open source platform that turns your Python scripts into shareable web apps in minutes with just a few lines of code. These interactive web apps are often the demo apps for most ML projects. The most fascinating thing about this platform is that it requires no front-end or back-end experience. Moreover, Streamlit lets you create these sites without HTML, CSS, or JavaScript, without defining routes and focusing on handling HTTP requests.

• OpenCV

  208 79,461 9.8 C++

  Open Source Computer Vision Library

  

  OpenCV is an open-source computer vision and machine learning software library that allows users to perform various ML tasks, from processing images and videos to identifying objects, faces, or handwriting. Besides object detection, this platform can also be used for complex computer vision tasks like Geometry-based monocular or stereo computer vision.

• MLflow

  68 18,909 9.9 Python

  Open source platform for the machine learning lifecycle

  

  MLflow is an open source platform for managing the machine learning project lifecycle, from model development to deployment and performance evaluation. It is beneficial for several reasons.

• metaflow

  28 8,313 9.5 Python

  Open Source Platform for developing, scaling and deploying serious ML, AI, and data science systems

  

  Metaflow is an open source framework developed at Netflix for building and managing ML, AI, and data science projects. This tool addresses the issue of deploying large data science applications in production by allowing developers to build workflows using their Python API, explore with notebooks, test, and quickly scale out to the cloud. ML experiments and workflows can also be tracked and stored on the platform.

• Kedro

  33 10,035 9.5 Python

  Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.

  

  Kedro is an ML development framework that brings data science projects from pilot development to production by creating reproducible, maintainable, and modular data science code. Kedro has a data catalog for data handling, support pipeline building, and a standardized template for code maintainability and consistency to effectively do this. Its data catalog uses lightweight data connectors to manage and track datasets. This allows you to use the same pipeline to build multiple production-level codes across your system.

• flyte

  36 5,829 9.8 Go

  Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.

  

  Flyte is an open source workflow orchestration platform that allows developers to build, transform, and deploy data and ML workflow through their Python SDK. This way, you can build and execute scalable, maintainable, reproducible pipelines for data processing and machine learning.

• example-get-started

  3 176 3.7 Python

  Get started DVC project (NLP, random forest)

  

  DVC can be described as the Git for your data science project. Data Version Control is a version control application that manages and tracks changes to data, ML models, and experiments. This way, you can keep your Git repository lightweight while ensuring your project can be reproduced at any point with the right data version. This is pretty similar to how Git tracks your code changes.

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