Maximize Your CPU Resources for XGBoost Training and Inference

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 runtime, 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 AI Tools
• Example training workloads that compare the performance of the latest XGBoost implementation on an end-to-end pipeline

Your hosts are AI technical engineers from Intel: Mecit Gungor and Rachel Oberman.

Get the AI Frameworks and Tools, which features six powerful tools and frameworks for numerical, scientific, and machine learning applications.

• Sign up for an Intel® Tiber™ AI Cloud 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 podcast—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. Available wherever you get your podcasts.

Mecit Gungor
AI technical consulting engineer and technical lead for oneAPI Technology Partner Training Program at Intel, Intel Corporation

Mecit works on various Intel® technologies that relate to machine learning and AI, and helps customers and partners use these technologies in their workloads. He holds a master's degree from Purdue, a bachelor's degree in 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 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.