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What is OpenVINO?

The Intel® Distribution of OpenVINO™ toolkit is an open-source resource for AI development that accelerates inference across a variety of Intel hardware. Designed to optimize AI workflows, this toolkit empowers developers to create sophisticated deep learning models for uses in computer vision, generative AI, and large language models. It comes with built-in model optimization features that ensure high throughput and low latency while reducing model size without compromising accuracy. OpenVINO™ stands out as an excellent option for developers looking to deploy AI solutions in multiple environments, from edge devices to cloud systems, thus promising both scalability and optimal performance on Intel architectures. Its adaptable design not only accommodates numerous AI applications but also enhances the overall efficiency of modern AI development projects. This flexibility makes it an essential tool for those aiming to advance their AI initiatives.

What is Auger.AI?

Auger.AI offers a robust solution aimed at ensuring the accuracy of machine learning models. Our Machine Learning Review and Monitoring (MLRAM) tool plays a crucial role in keeping your models' precision consistent over time. It also evaluates the return on investment for your predictive analytics efforts! MLRAM's compatibility with any machine learning technology stack enhances its flexibility and adaptability. Failing to regularly assess the accuracy of your machine learning system lifecycle could lead to financial losses due to inaccurate predictions. Additionally, the ongoing requirement for model retraining can be expensive and may not resolve issues related to concept drift. MLRAM provides substantial advantages for both data scientists and business stakeholders, including features such as accuracy visualization graphs, performance alerts, anomaly detection, and automated optimized retraining. The process of integrating your predictive model with MLRAM is incredibly straightforward, requiring just a single line of code. We also offer a free one-month trial of MLRAM for qualifying users. With Auger.AI, you can leverage the most reliable AutoML platform available, empowering your organization to enhance its predictive capabilities to the fullest. This comprehensive approach ensures that your machine learning initiatives remain efficient and cost-effective over time.

Media

Media

Integrations Supported

TensorFlow
Amazon Web Services (AWS)
Caffe
Cameralyze
EndoAim
Google Cloud Platform
Hugging Face
Intel Geti
Intel Open Edge Platform
Keras
LaunchX
Microsoft Azure
Nero AI
Nutanix Karbon Platform Services
ONNX
PaddlePaddle
PyTorch

Integrations Supported

TensorFlow
Amazon Web Services (AWS)
Caffe
Cameralyze
EndoAim
Google Cloud Platform
Hugging Face
Intel Geti
Intel Open Edge Platform
Keras
LaunchX
Microsoft Azure
Nero AI
Nutanix Karbon Platform Services
ONNX
PaddlePaddle
PyTorch

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

$200 per month
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Intel

Date Founded

1968

Company Location

United States

Company Website

www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html

Company Facts

Organization Name

Auger.AI

Date Founded

2019

Company Location

United States

Company Website

auger.ai/

Categories and Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Categories and Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

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