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Average Ratings 0 Ratings

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ease
features
design
support

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Description

Amazon EC2 Auto Scaling ensures that your applications remain available by allowing for the automatic addition or removal of EC2 instances based on scaling policies that you set. By utilizing dynamic or predictive scaling policies, you can adjust the capacity of EC2 instances to meet both historical and real-time demand fluctuations. The fleet management capabilities within Amazon EC2 Auto Scaling are designed to sustain the health and availability of your instance fleet effectively. In the realm of efficient DevOps, automation plays a crucial role, and one of the primary challenges lies in ensuring that your fleets of Amazon EC2 instances can automatically launch, provision software, and recover from failures. Amazon EC2 Auto Scaling offers vital functionalities for each phase of instance lifecycle automation. Furthermore, employing machine learning algorithms can aid in forecasting and optimizing the number of EC2 instances needed to proactively manage anticipated changes in traffic patterns. By leveraging these advanced features, organizations can enhance their operational efficiency and responsiveness to varying workload demands.

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.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

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

Integrations

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

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/ec2/autoscaling/

Vendor Details

Company Name

Amazon

Founded

2006

Country

United States

Website

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

Product Features

Infrastructure-as-a-Service (IaaS)

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

Alternatives

Alternatives

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