AI Analytics Part 3: Walk through the Steps to Optimize End-to-End Machine Learning Workflows

In This Series:

AI Analytics Part 1: Optimize End-to-End Data Science and Machine Learning Acceleration

AI Analytics Part 2: Enhance Deep Learning Workloads on 3rd Generation Intel® Xeon® Scalable Processors

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® 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.

Download the Intel AI Analytics Toolkit for Linux*.

Learn More

• Read the latest Intel® AI Analytics blogs on Medium.
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Meghana Rao
oneAPI and AI evangelist, Intel Corporation

Meghana is an experienced software developer who performs two distinct roles: a technical marketing engineer and an AI developer evangelist. She works with developers to evangelize Intel’s AI, IoT, and oneAPI products and solutions. Meghana 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 geographical information system (GIS) framework. Anant holds a bachelor’s degree in computer science from BITS Pilani, a master's 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.