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).

Topics covered:

  • 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.

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Download the Intel® oneAPI AI Analytics Toolkit for Linux*.

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Other Resources

  • 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.

Meghana Rao
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.

 

Anant Sinha
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.

 

Rachel Oberman
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.

 

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.

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