Average Ratings 0 Ratings
Average Ratings 0 Ratings
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
No
API Access
Has API
No
Integrations
Amazon EC2
Yes
Amazon Web Services (AWS)
Yes
Amazon EC2 G4 Instances
No
Amazon EC2 UltraClusters
Yes
MXNet
No
Observe
Yes
PyTorch
No
TensorFlow
No
Integrations
Amazon EC2
Yes
Amazon Web Services (AWS)
Yes
Amazon EC2 G4 Instances
Yes
Amazon EC2 UltraClusters
No
MXNet
Yes
Observe
No
PyTorch
Yes
TensorFlow
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
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
Product Features
Infrastructure-as-a-Service (IaaS)
Analytics / Reporting
No
Configuration Management
No
Data Migration
No
Data Security
No
Load Balancing
No
Log Access
No
Network Monitoring
No
Performance Monitoring
No
SLA Monitoring
No