Loading Events

« All Events

  • This event has passed.

Maximize Your CPU Resources for XGBoost* Training and Inference

March 3 @ 9:00 AM - 10:00 AM

AbdulMecit Gungor 200x200 1

Increase your machine learning model accuracy and performance with Intel-optimized XGBoost algorithms found in the Intel® AI Analytics Toolkit.

Read More 

Gradient boosting has many real-world applications as a general-purpose machine learning technique for regression, classification, and page ranking problems. It’s a common choice for large problem sizes, yet training implementation of this method is quite complex because of the multiple kernel dependencies that impact execution time, irregular memory access, and many other issues.

If this resonates with you, register for this session to learn about Intel’s optimizations for XGBoost, with specific focus on:

  • how to speed up your boosting algorithm workloads with the Intel® AI Analytics Toolkit, powered by oneAPI
  • example training workloads that compare the performance of the latest XGBoost implementation on an end-to-end pipeline

Your hosts are Intel AI Technical Engineers Mecit Gungor and Rachel Oberman.

Sign up.

Download the software
Get the Intel® AI Analytics Toolkit which features six powerful tools and frameworks for numerical, scientific, and machine learning applications.

Resources

  • Sign up for an Intel® DevCloud for oneAPI account—a free development sandbox with access to the latest Intel® hardware and oneAPI software.
  • Explore oneAPI, including developer opportunities and benefits
  • 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. Available wherever you get your podcasts.

Mecit Gungor, AI Technical Consulting Engineer, Intel Corporation

Mecit is an AI Technical Consulting Engineer and Tech Lead for oneAPI Technology Partner Training Program at Intel. He works on various Intel technologies that pertain to machine learning and artificial intelligence, and help customers, partners leverage these technologies in their workloads. He holds a Master’s degree from Purdue, a Bachelor of Electronics Engineering and a minor degree in Mathematics from City University of Hong Kong along with the S. H. Ho Foundation Academic achievement reward.

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 in Virginia.

Share this post via: