AI Analytics PART 3: Walk Through the Steps to Optimize End-to-End Machine Learning Workflows

Part 3 of this 3-part series shifts to “hands-on”, with presenters demonstrating the steps needed to execute key machine learning end-to-end workflows using the Intel® AI Analytics Toolkit.

Topics covered:

  • Highlighting 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 ML 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.
  • Showing the AI Kit’s ease of use and comprehensive nature as an enterprise analytics solution.
  • Demonstrating how to quickly test performance with a pre-built and externally available Jupyter notebook.

Get the software
Download the Intel® AI Analytics Toolkit for Linux. Find out more. Download now.

Other resources

  • Read the latest Intel 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 bi-weekly episode features industry VIPs who are blazing new trails through today’s data-centric world. Listen and subscribe today.
Meghana Rao, oneAPI & AI Evangelist, Intel Corporation

Meghana is an experienced software developer who wears two distinct hats: a technical marketing engineer and an IA developer evangelist. In her current role, she works with developers in evangelizing Intel’s AI, IoT, and oneAPI products and solutions. She is a technical speaker and author who is passionate about tech 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 is a Software Application Engineer who works with developers, helping them optimize their deep learning and machine learning applications for Intel architectures. Prior to joining Intel in 2018, he spent nearly 10 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, Masters of Engineering in Computer Science from Cornell University, and Masters of Science in Computer Science from University of California, Riverside.

Rachel Oberman, AI Technical Consulting Engineer, Intel Corporation

Rachel is an AI Technical Consulting Engineer who 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.

For more complete information about compiler optimizations, see our Optimization Notice.