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 the Intel® oneAPI AI Analytics Toolkit
- 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.
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Get the Intel® oneAPI AI Analytics Toolkit, which features six powerful tools and frameworks for numerical, scientific, and machine learning applications.
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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 degree from Purdue, a bachelor 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.
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 degree in computer science and data science from the College of William & Mary in Virginia.