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Solutions Snapshot
Intel® AI Builders - Solutions Snapshot
Natural Language Processing advancements have escalated with the availability of powerful computing at lower costs, but training on GPUs is expensive. Lilt, an AI-powered enterprise translation software and services company, wanted to optimize training tasks using CPUs instead of GPUs. By optimizing TensorFlow on Intel Xeon 8380 processors, Lilt was able to increase increasing inference by nearly 4X and deploy their workloads on Google Cloud N2 high memory instances with Intel Optimizations for TensorFlow 2.4.
Categories: 
Application Type - Deep Learning | Compute - Intel® Xeon® Scalable processor | Deployment Channel - CSP - Google Cloud, Hybrid Cloud | Industry - Cross-Industry, Government, Professional and Business Services | Intel® AI Analytics Toolkit powered by oneAPI - Intel® Optimization for TensorFlow* | Model Training - Models cant be re-trained - Inference only, Models can be trained - online learning | Operating System - Linux | Solution Geographic Availability - Worldwide | Solution Type - AI Software/SaaS | Topology - GNMT | Use Case - Other (pls specify)

Natural Language Processing advancements have escalated with the availability of powerful computing at lower costs, but training on GPUs is expensive. Lilt, an AI-powered enterprise translation software and services company, wanted to optimize training tasks using CPUs instead of GPUs. By optimizing TensorFlow on Intel Xeon 8380 processors, Lilt was able to increase increasing inference by nearly 4X and deploy their workloads on Google Cloud N2 high memory instances with Intel Optimizations for TensorFlow 2.4.

White Paper
Intel® AI Builders - White Paper
Powered by Intel AI, Mindtree’s Cognitive Customer Service suite helps enterprises transform their omni-channel customer service via intelligent agent assistance, faster issue resolution, personalization and much more. This White paper shows how MindTree’s AI-powered implementation of contact centers using Intel Distribution of Python and SigOpt delivers amazing performance and productivity of the entire conversational AI pipeline.
Categories: 
Application Type - Machine Learning, Deep Learning | Compute - Intel® Xeon® Scalable processor | Deployment Channel - On-premise (Private Cloud, Other), Hybrid Cloud | Industry - Cross-Industry | Intel® AI Analytics Toolkit powered by oneAPI - Intel® Optimization for PyTorch*, Intel® Distribution for Python* | Intel® Distribution of OpenVINO™ Toolkit powered by oneAPI - Intel® Distribution of OpenVINO™ Toolkit powered by oneAPI | Model Training - Models can be trained - data input only required, Models can be trained - online learning | Operating System - Linux | Solution Geographic Availability - France, Germany, India, Japan, United Kingdom, United States, Other - Asia Pacific, Other - Europe and Africa, Other - North and South America | Solution Type - AI Platform as a Service (AI PaaS), AI Software/SaaS | Topology - LSTM, BERT, Other | Use Case - Conversational Bots and Voice Agents

Powered by Intel AI, Mindtree’s Cognitive Customer Service suite helps enterprises transform their omni-channel customer service via intelligent agent assistance, faster issue resolution, personalization and much more. This White paper shows how MindTree’s AI-powered implementation of contact centers using Intel Distribution of Python and SigOpt delivers amazing performance and productivity of the entire conversational AI pipeline.

Solution Brief
Intel® AI Builders - Solution Brief
Flutura 's PCB (printed circuit board) Defect Detection is a real-time system with automated failure analysis of PCB manufacturing. Flutura converted their PCB defect detection models for OpenVINO and optimized them for parallel processing and post-quantization. They evaluated performance running on 2nd Gen Intel Xeon Scalable processor- and 3rd Gen Intel Xeon Scalable processor-based systems achieving significant performance improvement.
Categories: 
Application Type - Deep Learning | Compute - Intel® Xeon® Scalable processor | Deployment Channel - CSP - Amazon Web Services, CSP - Google Cloud, CSP - Microsoft Azure, On-premise (Private Cloud, Other), Hybrid Cloud | Industry - Defense and Space, Manufacturing | Intel® AI Analytics Toolkit powered by oneAPI - Intel® Optimization for TensorFlow* | Intel® Distribution of OpenVINO™ Toolkit powered by oneAPI - Intel® Distribution of OpenVINO™ Toolkit powered by oneAPI | Model Training - Models cant be re-trained - Inference only | Operating System - Linux, Other (pls specify) | Solution Geographic Availability - Worldwide | Solution Type - AI Software/SaaS | Use Case - Anomaly Detection

Flutura 's PCB (printed circuit board) Defect Detection is a real-time system with automated failure analysis of PCB manufacturing. Flutura converted their PCB defect detection models for OpenVINO and optimized them for parallel processing and post-quantization. They evaluated performance running on 2nd Gen Intel Xeon Scalable processor- and 3rd Gen Intel Xeon Scalable processor-based systems achieving significant performance improvement.

Solution Brief
Intel® AI Builders - Solution Brief
This brief highlights the EdgeVerve XtractEdge, a purpose-built document extraction, processing, and comprehension platform that unlocks business value from enterprise data by extracting intelligence from enterprise documents, regardless of complexity or domain specificity. The platform automates the end-to-end document processing lifecycle from ingestion to consumption, using AI capabilities such as Natural Language Processing (NLP), Computer Vision, and AI powered search utilizing Intel technologies to reduce time to value and operationalize models at enterprise scale.
Categories: 
Application Type - Deep Learning | Compute - Intel® Xeon® Scalable processor | Deployment Channel - CSP - Amazon Web Services, CSP - Google Cloud, CSP - Microsoft Azure, CSP - Other | Industry - Cross-Industry, Finance and Insurance, Healthcare | Intel® AI Analytics Toolkit powered by oneAPI - Intel® Optimization for TensorFlow*, Intel® Optimization for PyTorch* | Intel® Distribution of OpenVINO™ Toolkit powered by oneAPI - Intel® Distribution of OpenVINO™ Toolkit powered by oneAPI | Model Training - Models can be trained - data input only required, Models can be trained - requires labeled data | Operating System - Linux | Solution Geographic Availability - Worldwide | Solution Type - AI Platform as a Service (AI PaaS), AI Software/SaaS | Topology - ResNet50, SSD-VGG16, SSD, LSTM, RNN, BERT, Faster RCNN, Other, Yolo | Use Case - Image / Object Detection / Recognition / Classification, Data Preparations and Management, Document Management

This brief highlights the EdgeVerve XtractEdge, a purpose-built document extraction, processing, and comprehension platform that unlocks business value from enterprise data by extracting intelligence from enterprise documents, regardless of complexity or domain specificity. The platform automates the end-to-end document processing lifecycle from ingestion to consumption, using AI capabilities such as Natural Language Processing (NLP), Computer Vision, and AI powered search utilizing Intel technologies to reduce time to value and operationalize models at enterprise scale.

Solution Brief
Intel® AI Builders - Solution Brief
This Solution Brief highlights how Intel optimizations of Winning Health’s Bone Age Assessment (BAA) model helped greatly reduce image analysis time enabling large scalability of SaaS solution for hospitals and clinicians on cloud platforms.
Categories: 
Application Type - Deep Learning | Compute - Intel® Xeon® Scalable processor | Deployment Channel - CSP - Other, On-premise (Private Cloud, Other) | Industry - Healthcare | Intel® AI Analytics Toolkit powered by oneAPI - Intel® Optimization for PyTorch* | Intel® Distribution of OpenVINO™ Toolkit powered by oneAPI - Intel® Distribution of OpenVINO™ Toolkit powered by oneAPI | Model Training - Models cant be re-trained - Inference only | Operating System - Linux | Solution Geographic Availability - China (PRC) | Solution Type - AI Software/SaaS | Topology - Proprietary, Other | Use Case - Medical imaging, analysis and diagnostics

This Solution Brief highlights how Intel optimizations of Winning Health’s Bone Age Assessment (BAA) model helped greatly reduce image analysis time enabling large scalability of SaaS solution for hospitals and clinicians on cloud platforms.

Solution Brief
Intel® AI Builders - Solution Brief
This Solution Brief highlights the Winning Health AI-powered medical imaging system, jointly optimized by Intel, Winning Health and AMAX (a global OEM.) The paper cites lung nodule detection as an example to demonstrate how Intel 2nd Generation Xeon processors coupled with Intel AI software e.g., DL Boost, OpenVINO, improve the inference performance in terms of speed and accuracy.
Categories: 
Application Type - Deep Learning | Compute - Intel® Xeon® Scalable processor | Deployment Channel - CSP - Other, On-premise (Private Cloud, Other) | Industry - Healthcare | Intel® AI Analytics Toolkit powered by oneAPI - Intel® Optimization for PyTorch* | Intel® Distribution of OpenVINO™ Toolkit powered by oneAPI - Intel® Distribution of OpenVINO™ Toolkit powered by oneAPI | Model Training - Models cant be re-trained - Inference only | Operating System - Linux | Solution Geographic Availability - China (PRC) | Solution Type - AI Software/SaaS | Topology - Proprietary, Other | Use Case - Medical imaging, analysis and diagnostics

This Solution Brief highlights the Winning Health AI-powered medical imaging system, jointly optimized by Intel, Winning Health and AMAX (a global OEM.) The paper cites lung nodule detection as an example to demonstrate how Intel 2nd Generation Xeon processors coupled with Intel AI software e.g., DL Boost, OpenVINO, improve the inference performance in terms of speed and accuracy.

Solution Brief
Intel® AI Builders - Solution Brief
This brief discusses how the TietoEVRY edge reference platform is a viable alternative to existing commercial edge solutions to improve total cost of ownership (TCO) without compromising on performance. The platform, due its flexibility, scalability, cloud-native characteristic and Intel technology is powerful for emerging edge use cases across various industries such as Telecom, Industry, Enterprise, IoT, SmartCities, SmartHomes, Automotive or MedTech.
Categories: 
Application Type - Deep Learning | Compute - Intel® Xeon® Scalable processor, Intel® Movidius™ Vision Processing Units (VPU) | Deployment Channel - On-premise (Private Cloud, Other) | Industry - Automotive, Cross-Industry, Government | Intel® AI Analytics Toolkit powered by oneAPI - Intel® Optimization for TensorFlow* | Intel® Distribution of OpenVINO™ Toolkit powered by oneAPI - Intel® Distribution of OpenVINO™ Toolkit powered by oneAPI | Model Training - Models cant be re-trained - Inference only | Operating System - Linux | Solution Geographic Availability - Worldwide | Solution Type - AI Software/SaaS | Topology - SSD, MobileNet | Use Case - Image / Object Detection / Recognition / Classification, Smart City, Video Surveillance and Analytics

This brief discusses how the TietoEVRY edge reference platform is a viable alternative to existing commercial edge solutions to improve total cost of ownership (TCO) without compromising on performance. The platform, due its flexibility, scalability, cloud-native characteristic and Intel technology is powerful for emerging edge use cases across various industries such as Telecom, Industry, Enterprise, IoT, SmartCities, SmartHomes, Automotive or MedTech.

Solution Brief
Intel® AI Builders - Solution Brief
This brief discusses optimizing Matroid’s Similarity Search object detection model for Intel Xeon Scalable processors has achieved increased performance and could open new doors for more flexible and possibly lower cost services and deployments.
Categories: 
Application Type - Deep Learning, Other | Compute - Intel® Xeon® Scalable processor | Deployment Channel - CSP - Amazon Web Services, CSP - Google Cloud, CSP - Microsoft Azure, CSP - Other, On-premise (Private Cloud, Other), Hybrid Cloud | Industry - Arts and Entertainment, Cross-Industry, Retail, Communications | Intel® AI Analytics Toolkit powered by oneAPI - Intel® Optimization for TensorFlow*, Intel® Distribution for Python* | Model Training - Models cant be re-trained - Inference only | Operating System - Linux | Solution Geographic Availability - Worldwide | Solution Type - AI Software/SaaS | Topology - Other | Use Case - Image / Object Detection / Recognition / Classification, Facial Detection / Recognition / Classification, Video Surveillance and Analytics | Solution Geographic Availability - Worldwide

This brief discusses optimizing Matroid’s Similarity Search object detection model for Intel Xeon Scalable processors has achieved increased performance and could open new doors for more flexible and possibly lower cost services and deployments.

Solution Brief
Intel® AI Builders - Solution Brief
This brief discusses Seassoon’s text detection solution which inferences data input for cognitive decision-making. It highlights how utilizing Intel Optimizations for PyTorch and Image Detection enabled Seassoon to achieve 3X faster Inferencing and avoid the need for a more costly and complex GPU solution.
Categories: 
Application Type - Machine Learning | Compute - Intel® Xeon® Scalable processor | Deployment Channel - On-premise (Private Cloud, Other) | Industry - Cross-Industry, Energy and Utilities, Finance and Insurance, Government | Intel® AI Analytics Toolkit powered by oneAPI - Intel® Optimization for PyTorch* | Intel® Distribution of OpenVINO™ Toolkit powered by oneAPI - Intel® Distribution of OpenVINO™ Toolkit powered by oneAPI | Operating System - Linux | Solution Geographic Availability - China (PRC) | Solution Type - AI Platform as a Service (AI PaaS) | Topology - Proprietary, Other | Use Case - Image / Object Detection / Recognition / Classification, Data Preparations and Management, Document Management

This brief discusses Seassoon’s text detection solution which inferences data input for cognitive decision-making. It highlights how utilizing Intel Optimizations for PyTorch and Image Detection enabled Seassoon to achieve 3X faster Inferencing and avoid the need for a more costly and complex GPU solution.

Solution Brief
Intel® AI Builders - Solution Brief
This brief discusses ICETech vision computing-based systems that automatically identify vehicles and license plates in unattended smart parking operations, allowing them to run more efficiently. Optimizing for OpenVINO and quantizing for INT8 using the Post Training Optimization toolkit (POT) and inferencing with Intel DL Boost (VNNI) improved ICETech’s inferencing performance with minimal impact on accuracy.
Categories: 
Application Type - Deep Learning | Compute - Intel® Xeon® Scalable processor | Deployment Channel - On-premise (Private Cloud, Other) | Industry - Transportation and Warehousing | Intel® AI Analytics Toolkit powered by oneAPI - Intel® Distribution for Python* | Intel® Distribution of OpenVINO™ Toolkit powered by oneAPI - Intel® Distribution of OpenVINO™ Toolkit powered by oneAPI | Model Training - Models cant be re-trained - Inference only | Operating System - Linux | Solution Geographic Availability - Brazil, China (PRC), India, Korea, Taiwan, Other - Asia Pacific, Other - Europe and Africa, Other - North and South America | Topology - SSD-VGG16, MobileNet | Use Case - Image / Object Detection / Recognition / Classification, Smart City

This brief discusses ICETech vision computing-based systems that automatically identify vehicles and license plates in unattended smart parking operations, allowing them to run more efficiently. Optimizing for OpenVINO and quantizing for INT8 using the Post Training Optimization toolkit (POT) and inferencing with Intel DL Boost (VNNI) improved ICETech’s inferencing performance with minimal impact on accuracy.