Accelerate Data Science & AI Pipelines

AI is found in almost every industry vertical, with applications and use cases that are transforming business and conferring competitive advantage. 

The Intel® AI Analytics Toolkit gives developers, researchers, and data scientists familiar Python* tools to accelerate each step in the pipeline—training deep neural networks, integrating trained models into applications for inference, and executing functions for data analytics and machine learning workloads.

Using this toolkit, you can:

• Deliver high-performance deep learning (DL) training on CPUs and integrate fast inference into your AI applications with Intel-optimized DL frameworks: TensorFlow* and PyTorch*.
• Achieve drop-in acceleration for data analytics and machine learning workflows with compute-intensive Python* packages – Pandas, NumPy, SciPy, Scikit-learn*, and XGboost*.
  

The components of the toolkit are built using optimized oneAPI libraries to accelerate the end-to-end workflow on Intel® architectures.

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Develop in the Cloud

Get what you need to build, test, and optimize your oneAPI projects for free. With an Intel® DevCloud account, you get 120 days of access to the latest Intel® hardware—CPUs, GPUs, FPGAs—and Intel oneAPI tools and frameworks. No software downloads. No configuration steps. No installations.

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Optimized Deep Learning Frameworks

• Leverage popular, Intel-optimized frameworks—including TensorFlow and PyTorch—to use the full power of Intel® architecture and yield high performance for training and inference.
• Expedite development by using the open source pretrained machine learning models that are optimized by Intel for best performance. 

High-Performance Python*

• Take advantage of the most popular and fastest growing programming language for AI and data analytics.
• Use its drop-in, compute-intense packages—optimized for Intel® architecture and underlying instruction sets—to accelerate AI applications and maximize performance.

Data Analytics & Machine Learning Acceleration

• Implement data science and analytics pipelines from preprocessing through machine learning.
• Scale-out efficiently using the high-performing Python packages, such as pandas, XGBoost, and scikit-learn*.
• Analyze large data sets with available compute resources and make better predictions faster using the high-speed algorithms.

Intel® Optimization for TensorFlow*^(Beta)

Train and infer high-performing, large-scale machine learning models with this Intel-optimized deep-learning framework that's based on Python.
 

PyTorch Optimized for Intel® Technology

Use this Python-based scientific computing package for deep learning training and inference workloads.
 

Intel® Distribution for Python*^(Beta) 

Achieve fast math-intensive workload performance without code changes for data science and machine learning problems.

Model Zoo for Intel® Architecture

Access pretrained models, sample scripts, best practices, and step-by-step tutorials for many popular open source machine learning models optimized by Intel to run on Intel® Xeon® Scalable processors.
 

Intel® AI Quantization Tools for TensorFlow*

Quantize models trained with FP32 precision to int8 precision and accelerate deep learning inference workflows.