Amazon Elastic Inference Description

Amazon Elastic Inference provides an affordable way to enhance Amazon EC2 and Sagemaker instances or Amazon ECS tasks with GPU-powered acceleration, potentially cutting deep learning inference costs by as much as 75%. It is compatible with models built on TensorFlow, Apache MXNet, PyTorch, and ONNX. The term "inference" refers to the act of generating predictions from a trained model. In the realm of deep learning, inference can represent up to 90% of the total operational expenses, primarily for two reasons. Firstly, GPU instances are generally optimized for model training rather than inference, as training tasks can handle numerous data samples simultaneously, while inference typically involves processing one input at a time in real-time, resulting in minimal GPU usage. Consequently, relying solely on GPU instances for inference can lead to higher costs. Conversely, CPU instances lack the necessary specialization for matrix computations, making them inefficient and often too sluggish for deep learning inference tasks. This necessitates a solution like Elastic Inference, which optimally balances cost and performance in inference scenarios.

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Company Details

Company:
Amazon
Year Founded:
2006
Headquarters:
United States
Website:
aws.amazon.com/machine-learning/elastic-inference/

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Product Details

Platforms
Web-Based
Types of Training
Training Docs
Customer Support
Online Support

Amazon Elastic Inference Features and Options

Infrastructure-as-a-Service (IaaS) Provider

Analytics / Reporting
Configuration Management
Data Migration
Data Security
Load Balancing
Log Access
Network Monitoring
Performance Monitoring
SLA Monitoring

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