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Accelerate AI and Data Science for GPU and Intel® Gaudi®

Lower AI Costs with Intel® Tiber™ App-Level Optimization

App-Level Optimization enhances performance across data pipelines by leveraging oneAPI libraries for GPU-based workloads and runtime optimization on CPUs to maximize efficiency on Intel® architecture for cost effective AI.

Key Benefits

Seamless Integration

Utilize Intel-optimized frameworks like TensorFlow and PyTorch for efficient model training and inference.

High-Performance Libraries

Accelerate Python libraries like scikit-learn and XGBoost for faster data processing.

Easy Deployment

Deploy and optimize workloads across Intel CPUs and GPUs effortlessly.

Implementation Steps

1. Install

Quickly install App-Level Optimization on your AI workloads

2. Learn

App-Level Optimization analyzes your application workloads to learn specific patterns 

03. Deploy

Activate App-Level Optimization and experience immediate performance improvements and cost reduction

Deep LearningModeling, training, transfer learning, fine-tuning, inferencePyTorch* Optimizations from Intel®TensorFlow* Optimizations from IntelOptimization & InferencePerform model compression and deploy to the cloud, edge, or PCIntel® Neural CompressorOpenVINOTM ToolkitMachine LearningTraining and predictionIntel® Extension for Scikit-learn*Intel® Optimization for XGBoost*Data analyticsDistributed data ingestion and preprocessingModin*AI Tools from Intel®AI Reference ModelsPretrained, Intel-optimized modelsIntel® Tiber™ AI StudioFull-stack machine learning operating systemIntel® Gaudi® SoftwareLeverage dedicated AI acceleratorsHardware support varies by each individual tool. Additional architecture support will be expanded over time. *Other names and brands may be claimed as the property of others. CPUGPUAI Accelerator
AI Tools from Intel®Data analyticsDistributed data ingestion and preprocessingModin*Machine LearningTraining and predictionIntel® Extension for Scikit-learn*Intel® Optimization for XGBoost*Deep LearningModeling, training, transfer learning, fine-tuning, inferencePyTorch* Optimizations from Intel®TensorFlow* Optimizations from IntelOptimization & InferencePerform model compression and deploy to the cloud, edge, or PCIntel® Neural CompressorOpenVINOTM ToolkitAI Reference ModelsPretrained, Intel-optimized modelsIntel® Tiber™ AI StudioFull-stack machine learning operating systemIntel® Gaudi® SoftwareLeverage dedicated AI acceleratorsCPUGPUAI AcceleratorHardware support varies by each individual tool. Additional architecture support will be expanded over time. *Other names and brands may be claimed as the property of others.

AI Optimization Features

Optimized Deep Learning

Harness Intel-optimized frameworks like TensorFlow and PyTorch, alongside pretrained ML models and precision tuning, to maximize performance across training and inference on Intel® architecture.

Data Analytics and Machine Learning Acceleration

Boost machine learning model accuracy and performance using optimized algorithms in scikit-learn, XGBoost,and RAG (Retrieval-Augmented Generation) with seamless scalability across clusters via Intel Extension for Scikit-learn

High-Performance Python

Leverage optimized Python performance for AI and analytics, processing large scientific datasets faster and more efficiently with enhanced multithreading and memory management.

Simplified Scaling across Multi-node DataFrames

Scale pandas workflows to multi-core and multi-node setups with a single code change using Modin, accelerating data analytics with powerful compute engines like Ray, Dask*, and MPI.

American Airlines Case Study

American Airlines spoke about their experience with App-Level Optimization on their Data Lakes dedicated to data processing and analytics. Because Data Lakes has a limited number of node connections allowed, the reduced job completion time provided by App-Level Optimization allowed their engineers to process and analyze data at an accelerated pace and with greater scalability.

“Our data teams are now able to use Data Lake as the platform meant for it to be used.”

Vijay Premkumar, Sr. Manager – Cloud Platform Innovation at American Airlines