Open-source projects on GitHub are probably the best way for developers to dive into technology development and learn while contributing, and the AI/ML field is no different. More and more people are interested in building AI/ML models, and some big tech companies have also opened their source code to further progress.
Here is a list of these 12 trending and open source projects for you to try!
The TensorFlow platform is used for data automation, model tracking and retraining, and performance monitoring. It was developed by the Google Brain team and has around 150,000 active contributors. The flexibility of this model makes it very popular among developers who are included in their projects.
GitHub repositories allow users to build in the cloud or mobile devices for convenience. These algorithms are used for many computer vision-based applications such as image classification, speech recognition, segmentation, and more.
Click here for the GitHub repository.
Computer vision is probably the most requested tool for artificial intelligence—from robots to autonomous vehicles. OpenCV offers a large open-source repository for using computer vision in machine learning tasks and is available to Python developers.
The repository has more than 2500 algorithms related to computer vision and big tech companies like Google, IBM, and Intel use it in many of their development projects.
Click here to check out the code.
The GitHub community has over 100K stars with contributors actively improving. The platform has a declarative view that allows easy understanding and modification for developers.
Contribute to React-Native code.
The text-to-image model is the talk of the town and DALL-E released its code on GitHub, the possibilities are now endless. To find out how machine learning models work, start by plugging a word into DALL-E and see how the machine “thinks”.
The open-source project on GitHub is the official PyTorch package used in DALL-E for discrete VAEs, although the package does not include the transformers used to generate the images.
Click here to check out the code.
When it comes to autonomous vehicles, robotics, or any other vision-based device, object detection is one of the core parts of the system. YOLOv7—being one of the fastest and most accurate object detection algorithms—provides the perfect solution.
The GitHub repository contains packages for developers to implement in their projects and train the model by providing a collection of images, and the model starts its detection process.
Here’s a link to the GitHub repository.
K8s was developed by Google to allow developers to manage their container applications across all platforms. Automated systems help in scaling, developing, and managing applications.
With over 70,000 stars on GitHub, it is one of the most popular repositories built by Google. A global leader in containerized packaging services, K8s is organized by the Cloud Native Computing Foundation (CNCF).
Click here to check out the K8s code.
Another one developed by Google, Flutter is a software development kit (SDK) that allows developers to build applications from a single code base using a toolkit of interfaces. It is powered by Skia, thus, the app is compatible with PC, web, and mobile platforms.
The 100,000-star community on GitHub is compatible with Android and Chromium-based apps and witbiOS and is effective for seamlessly integrating graphics, text, and video overlays without messing with code.
Click here to dive into Flutter.
This Java-based automation server has over 1800 plugins to automate almost anything. It is an easy-to-use and useful framework for building, testing, and deploying applications and offers integration and a delivery environment that can be customized to all languages.
Jenkins is also useful for static code analysis and detecting bugs in models. With this server, developers can execute and automate dull and repetitive tasks and focus on building things that machines cannot.
To deploy Jenkins in your project, click here.
Started by RedHat in 2016, Ansible is an automation platform for developers to configure systems, manage networks, and deploy their software. The setup is quite simple with almost no learning curve, and therefore attracts attention and contributions.
With around 55,000 stars, Ansible is actively accepting contributions and updates as its approach is similar to plain English.
To check the code for the automation platform, click here.
An open-source ML framework that allows publishing algorithms, event collection and evaluation, and querying prediction results. It is based on HBase, Hadoop, Spark, and Elasticsearch and creates prediction engines for machine learning projects.
The project has more than 12,000 stars on GitHub and generates predictive results via a REST API.
Click here for the code.
The fastest performance gradient boosting framework based on the decision tree algorithm, LightGBM, is mainly used for classification, ranking, among other machine learning tasks. It supports large-scale data and parallel learning with multiple GPUs—making it more attractive than other competitors.
Developed by Microsoft, LightGBM has around 14,000 stars on GitHub and is actively accepting contributions from developers.
Click here to gradient boost your project.
This open source project analyzes, searches, and stores large amounts of data in real-time. This can be integrated for projects that require data management such as logs, metrics, endpoint security, search backend, and application monitoring.
With more than 60,000 stars, Elasticsearch has been letting developers build and improve the backend of various search engines in their projects.
Click here to find the code.