AI is disrupting every industry. From enabling data-driven automation to power sustainable smart factories, to detecting production errors on the production floor, to using robots for e-commerce order fulfillment, and even fighting wildfires—AI is almost everywhere.
As a result, more and more developers are interested in pursuing a career in AI or taking it to the next level. But getting into the field can be daunting, and many people don't know where to start. While there are countless resources available on the market, we've put together a collection of top AI frameworks and tools to provide developers with the essential components necessary to get started with AI development.
Caffe: Born at Berkeley AI Lab, Caffe is a deep learning framework focused on speed and modularity. It uses an expression architecture that allows developers to switch between CPU and GPU with just one flag. It has a lot of code and is designed to facilitate development. Its speed is ideal for research experiments or industrial applications that need to process millions of images per day. The project also provides developers with tutorials, installation instructions, and step-by-step examples they need to get started.
Keras: This popular AI framework is a neural network library written in Python. Keras prides itself on making it easy, flexible, and powerful for developers to experiment with machine learning. It reduces cognitive load, minimizes user actions, and clearly displays error messages during development. You can get started with the project's extensive documentation and developer guide.
MXNet: MXNet, currently an incubation project of the Apache Software Foundation, is a deep learning framework ideal for AI research, prototyping, and production. It includes a hybrid front end that allows developers to mix symbolic and imperative programming to maximize efficiency and productivity. Additional features and capabilities include scalable distributed training, support for eight language bindings, and an ecosystem of tools and libraries to extend MXNet use cases.
ONNX: As big tech companies work to make AI easier to use, ONNX ensures that developers can easily interoperate in an ecosystem of AI frameworks. It is not just a framework, but an open standard for machine learning interoperability. Developers can work in their favorite frameworks and inference engines, and ONNX is designed to eliminate any downstream impact.
PaddlePaddle: This open source deep learning platform is dedicated to providing rich AI capabilities for industrial use cases. It is widely used in fields such as manufacturing, agriculture and enterprise. The platform features support for declarative and imperative programming, large-scale training, multi-terminal and multi-platform deployment, and has rich algorithms and pre-trained models.
PyTorch: This deep learning research platform aims to shorten the time from prototyping to production. This project provides two advanced capabilities: tensor computation and deep neural networks. It was developed for deep integration into Python. Developers can use it like other popular Python packages like NumPy, SciPy, and scikit-learn. The framework requires minimal overhead to get started and integrates with acceleration libraries such as Intel® oneMKL for maximum speed.
OpenCV: The community around this open source computer vision library aims to make working with AI easy and fun. The project itself provides more than 2,500 computer vision and machine learning algorithms for developers to get started with. The OpenCV team also offers many tutorials, courses, and events designed to interact and collaborate with the AI community. Check out their latest AI quiz game show sponsored by Intel®OpenVINO™.
OpenVINO™: The Intel® OpenVINO™ toolkit is designed to optimize and deploy AI inference. Intel just released OpenVINO 2022.1, the biggest update since the toolkit was first released. It includes many new features designed to make life easier for AI developers. Key features include extended natural language processing support, device portability, and better inference performance. Developers can quickly get started with Open Model Zoo's pretrained models. Click here to learn more about the latest version.
TensorFlow: This Google-developed end-to-end deep learning platform is aimed at both beginners and development experts. Its core library is designed to help developers build and deploy machine learning models. But there are also additional libraries for JavaScript, mobile and IoT, and production development.
For more AI development resources, visit the Intel® 30-DayAI Developer Challenge to learn how to build AI applications at your own pace. To further advance your AI skills, consider the Intel® Edge AI DeveloperCertification Program.