Intel® AI Analytics Toolkit

Achieve End-to-End Performance for AI Workloads

In the News

LAIKA Studios & Intel Join Forces to Expand What’s Possible in Stop-Motion Filmmaking

See how LAIKA’s films and the company’s work with Intel’s Applied Machine Learning team and use of Intel® oneAPI tools helps it realize the limitless scope of stop-motion animation.

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An Open Road to Swift DataFrame Scaling

This podcast looks at the challenges of data preprocessing, especially time-consuming, data-wrangling tasks. It discusses how Intel and Omnisci are collaborating to provide integrated solutions that improve dataframe scaling.

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Machine Learning Performance Results for Deep Learning Training on a CPU

Reflecting the broad range of AI workloads, Intel submitted results for Machine Learning Performance Training Release v0.7 in June 2020 for three training topologies: MiniGo, DLRM, and ResNet-50 v1.5. Results in each case demonstrated that Intel continues to raise the bar for training on general purpose CPUs.

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Optimize XGBoost Training Performance

Compare the training performance of XGBoost 1.1 on a CPU with third-party GPUs. Learn more about the optimizations introduced to this popular gradient boosting trees algorithm.

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Intel and Facebook Accelerate PyTorch Performance

Harnessing the new bfloat16 capability in Intel® Deep Learning Boost, the team substantially improved PyTorch performance across multiple training workloads on 3rd generation Intel® Xeon® Scalable processors.

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Treatise of Medical Image Processing: COVID-19 Recognition

Read about a new proposal that uses an AI-based analytics system to detect COVID-19 from chest X-rays and CT radiographs.

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Product and Performance Information

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Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.