This final episode of the three-part series shifts to hands-on, with presenters demonstrating the steps needed to perform key machine-learning, end-to-end workflows using the Intel® oneAPI AI Analytics Toolkit (AI Kit).
- Highlight optimizations in key workflow components running on Intel® architecture, including:
- Intel’s integration of the OmniSciDB engine for Modin*—A library that helps speed pandas workflows by changing a single line of code.
- XGBoost—An optimized, distributed, gradient-boosting library that implements machine-learning algorithms under the gradient-boosting framework.
- Intel’s optimized implementation of scikit-learn*—A library of simple, efficient tools for predictive data analysis through the daal4py library.
- Show the ease of use of the AI Kit and its comprehensive nature as an enterprise analytics solution.
- Demonstrate how to quickly test performance with a prebuilt and externally available Jupyter* Notebook.
Get the Software
Download the Intel® oneAPI AI Analytics Toolkit for Linux*.
- Read the latest Intel oneAPI AI Analytics blogs on Medium.
- Develop in the Cloud—Sign up for an Intel® DevCloud account, a free development sandbox with access to the latest Intel® hardware and oneAPI software.
- Subscribe to the POD—Code Together is an interview series that explores the challenges at the forefront of cross-architecture development. Each biweekly episode features industry VIPs who are blazing new trails through today’s data-centric world. Listen and subscribe today.
oneAPI and artificial intelligence (AI) evangelist, Intel Corporation
Meghana is an experienced software developer who performs two distinct roles: a technical marketing engineer and an AI developer evangelist. In her current role, she works with developers to evangelize Intel’s AI, IoT, and oneAPI products and solutions. She is a technical speaker and author who is passionate about technology advocacy through training on advanced topics on Intel® technology. Meghana joined Intel in 2008 and holds a bachelor’s degree in computer science and engineering from Bangalore University, and a master's degree in engineering and technology management from Portland State University, Oregon.
Software applications engineer, Intel Corporation
Anant works with developers to help them optimize their deep-learning and machine-learning applications for Intel® architectures. Prior to joining Intel in 2018, he spent nearly ten years as a software product engineer and software developer for Esri*, a global market leader in the GIS (geographical information system) framework. Anant holds a bachelor’s degree in computer science from BITS Pilani, a masters of engineering degree in computer science from Cornell University, and a masters of science degree in computer science from University of California, Riverside.
AI technical consulting engineer, Intel Corporation
Rachel helps customers optimize their workflows with data analytics and machine-learning algorithms from Intel. Prior to joining Intel in 2019, she focused on geospatial analysis and data science, and founded geoLab—an undergraduate research lab, serving as its director. Rachel holds a bachelor’s degree in computer science and data science from the College of William & Mary.