AI applications must necessarily achieve a high bar. They must crunch, assess, and accurately visualize enormous, complex datasets in real-time. They must be parallelized and optimized to run across multiple architectures.
The Intel® oneAPI AI Analytics Toolkit (powered by oneAPI) is purpose-built to help AI developers and data scientists meet all of those challenges.
In this session, Saumya Satish, product manager, Intel, delivers an overview of the toolkit, including its complement of software tools and frameworks that enable development and deployment of machine and deep-learning models across XPUs.
The presentation includes:
- The toolkit’s collection of libraries and frameworks that are powered by oneAPI provide drop-in application acceleration to exploit the cutting-edge features of modern hardware
- How to maximize performance for model training, inference, and deployment
- Optimized machine-learning and data-analytics Python* packages
Get the Software
- Download the Intel oneAPI AI Analytics Toolkit—a collection of six libraries and frameworks for data science and AI pipelines.
- Sign up for an Intel® DevCloud account—a free development sandbox with access to the latest Intel® hardware and oneAPI software, including the Intel oneAPI AI Analytics Toolkit.
Product manager for AI software products with a focus on deep learning and data analytics technologies, Intel Corporation
Saumya is passionate about the developer ecosystem and is keen to provide the right set of tools that help developers build innovative applications, particularly AI, and machine-learning domains. Since joining Intel in 2011, she has worked as a research scientist and technical evangelist on some of Intel’s imaging and computer vision software products. Saumya holds a master degree in electrical engineering from University of Florida, Gainesville. A native of India, she is currently based in San Jose, California.
Intel® oneAPI AI Analytics Toolkit
Accelerate end-to-end machine learning and data science pipelines with optimized deep learning frameworks and high-performing Python* libraries.