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What is In Parallel?

The Intelligent Operating Model transcends traditional software, serving as a revolutionary solution that fundamentally alters your organization’s operational framework by integrating AI for a more modernized strategy. In Parallel’s offering of the Intelligent Operating Model presents an innovative method to align your organization’s internal processes with external conditions instantaneously. By consistently linking strategic planning with execution, this flexible model equips your organization to predict challenges and capitalize on opportunities, thereby driving transformative change and achieving unmatched operational efficiency. Employing advanced AI alongside real-time data integration, the Intelligent Operating Model significantly upgrades your current systems. It transforms workflows by eradicating inefficiencies, unlocking hidden potentials, and overcoming obstacles that hinder advancement, ensuring that your organization remains competitive and responsive to change.

What is Amazon Elastic Inference?

Amazon Elastic Inference provides a budget-friendly solution to boost the performance of Amazon EC2 and SageMaker instances, as well as Amazon ECS tasks, by enabling GPU-driven acceleration that could reduce deep learning inference costs by up to 75%. It is compatible with models developed using TensorFlow, Apache MXNet, PyTorch, and ONNX. Inference refers to the process of predicting outcomes once a model has undergone training, and in the context of deep learning, it can represent as much as 90% of overall operational expenses due to a couple of key reasons. One reason is that dedicated GPU instances are largely tailored for training, which involves processing many data samples at once, while inference typically processes one input at a time in real-time, resulting in underutilization of GPU resources. This discrepancy creates an inefficient cost structure for GPU inference that is used on its own. On the other hand, standalone CPU instances lack the necessary optimization for matrix computations, making them insufficient for meeting the rapid speed demands of deep learning inference. By utilizing Elastic Inference, users are able to find a more effective balance between performance and expense, allowing their inference tasks to be executed with greater efficiency and effectiveness. Ultimately, this integration empowers users to optimize their computational resources while maintaining high performance.

Media

Media

Integrations Supported

Integrations Supported

Amazon EC2
Amazon EC2 G4 Instances
Amazon Web Services (AWS)
MXNet
PyTorch
TensorFlow

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

24 Hour Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

In Parallel

Date Founded

2023

Company Location

Finland

Company Website

in-parallel.com

Company Facts

Organization Name

Amazon

Date Founded

2006

Company Location

United States

Company Website

aws.amazon.com/machine-learning/elastic-inference/

Categories and Features

Categories and Features

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