Open source projects related to artificial intelligence (AI) and machine learning (ML) have seen explosive growth in recent years. However, this growth is not without challenges and difficulties: the community is now faced with a series of questions and a lot of confusion, which even makes it complicated to understand the objectives of each individual project.
The advent of technologies like TensorFlow, PyTorch, and scikit-learn has made AI and ML accessible to a wide range of developers, spurring the creation of countless initiatives. The multiple projects gradually released, range from the implementation of basic algorithms to the release of pre-trained models and tools for developing and managing AI applications.
The Python libraries that support the birth of new open source projects in the field of AI and machine learning
TensorFlow, PyTorch and scikit-learn are three of the main ones open source libraries for machine learning and artificial intelligence, exploitable with language Python.
TensorFlow is a library developed by Google and is widely used for training deep neural networks and deep learning. It offers a broad range of tools for building, training, and deploying machine learning models on a variety of platforms, including CPU, GPU, and TPU.
PyTorch is a machine learning library developed by Meta. It is known for its flexibility and ease of use, especially for research and development of new deep learning models. It provides an intuitive interface for defining and training neural networks, with an emphasis on dynamic computation and GPU-powered acceleration.
scikit-learn provides a wide range of machine learning algorithms, including classification, regression, clustering, and dimensionality reduction. It is widely used in both academic and industrial settings for building traditional machine learning models.
The challenge of selection
As open source projects focused on AI and ML proliferate, developers often find themselves confused about which projects are best suited to their needs. specific needs. The diversity and complexity of the ecosystem make it difficult to orient yourself and identify the best available resources.
By the way, some projects and repositories quickly gain a large number of interested parties (GitHub stars), but then quickly lose popularity. The phenomenon can be attributed to a variety of factors, including changes in trends, the appearance of new technologies or competing designs, lack of development and ongoing maintenance.
An independent researcher has published his Open Source LLM Tools report: updated every 6 hours, it keeps track of the most promising AI and ML projects or those that are gaining great popularity. In fact, each initiative is carefully classified in terms of category and subcategory.
By clicking on the table headers, you can sort the various AI projects according to various criteria. For example, selecting the column stars then choosing “Sort 9…1“, you can get the projects that have obtained the highest number of “stars” on GitHub first.
AI Stack
The term “AI stack” refers to the set of software and infrastructure components needed to develop, train, deploy, and operate artificial intelligence applications. These components constitute a set of layers that interact with each other to support the whole life cycle of AI applications. Typically, the AI stack includes the following layers:
- Infrastructure: It is the foundation of the AI stack and includes the hardware and software resources needed to run AI models. In this layer we can include server systems, GPUs, development frameworks such as TensorFlow or PyTorch, supporting libraries such as CUDA for accelerating tasks using GPUs.
- Model development: This layer provides tools and frameworks to develop, train, and optimize AI models. It includes machine learning and deep learning frameworks such as TensorFlow, PyTorch, scikit-learn, as well as tools for optimization and data engineering.
- Application development: Here the focus is on creating specific applications that use the developed AI models. This includes frameworks and tools for developing user interfaces, model integrations into existing applications, and tools for managing and monitoring applications.
- Applications: This is the highest level of the “stack” that includes end applications capable of delivering AI functionality to end users. Applications for speech recognition, image recognition, machine translation, data analysis and much more belong to this layer.
More information on the study can be found on this page, which summarizes work that began 4 years ago. This is a precious effort that acts as a compass for successfully navigating an area full of opportunities but, at the same time, truly complex.
Opening image credit: iStock.com – da-kuk