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Description

Deep Learning Containers consist of Docker images that come preloaded and verified with the latest editions of well-known deep learning frameworks. They enable the rapid deployment of tailored machine learning environments, eliminating the need to create and refine these setups from the beginning. You can establish deep learning environments in just a few minutes by utilizing these ready-to-use and thoroughly tested Docker images. Furthermore, you can develop personalized machine learning workflows for tasks such as training, validation, and deployment through seamless integration with services like Amazon SageMaker, Amazon EKS, and Amazon ECS, enhancing efficiency in your projects. This capability streamlines the process, allowing data scientists and developers to focus more on their models rather than environment configuration.

Description

Amazon SageMaker Model Monitor enables users to choose which data to observe and assess without any coding requirements. It provides a selection of data types, including prediction outputs, while also capturing relevant metadata such as timestamps, model identifiers, and endpoints, allowing for comprehensive analysis of model predictions in relation to this metadata. Users can adjust the data capture sampling rate as a percentage of total traffic, particularly beneficial for high-volume real-time predictions, with all captured data securely stored in their designated Amazon S3 bucket. Additionally, the data can be encrypted, and users have the ability to set up fine-grained security measures, establish data retention guidelines, and implement access control protocols to ensure secure data handling. Amazon SageMaker Model Monitor also includes built-in analytical capabilities, utilizing statistical rules to identify shifts in data and variations in model performance. Moreover, users have the flexibility to create custom rules and define specific thresholds for each of those rules, enhancing the monitoring process further. This level of customization allows for a tailored monitoring experience that can adapt to varying project requirements and objectives.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amazon SageMaker Yes 
Amazon Web Services (AWS) Yes 
AWS Marketplace Yes 
AWS Neuron Yes 
Amazon EC2 G5 Instances Yes 
Amazon EC2 P4 Instances Yes 
Amazon EC2 P5 Instances Yes 
Amazon EC2 Trn1 Instances Yes 
Amazon EKS Yes 
Amazon Elastic Container Registry (ECR) Yes 
Amazon Elastic Container Service (Amazon ECS) Yes 

Integrations

Amazon SageMaker Yes 
Amazon Web Services (AWS) Yes 
AWS Marketplace No 
AWS Neuron No 
Amazon EC2 G5 Instances No 
Amazon EC2 P4 Instances No 
Amazon EC2 P5 Instances No 
Amazon EC2 Trn1 Instances No 
Amazon EKS No 
Amazon Elastic Container Registry (ECR) No 
Amazon Elastic Container Service (Amazon ECS) No 

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 No 
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 No 
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

2006

Country

United States

Website

aws.amazon.com/machine-learning/containers/

Vendor Details

Company Name

Amazon

Founded

2006

Country

United States

Website

aws.amazon.com/sagemaker/model-monitor/

Product Features

Container Management

Access Control No 
Application Development No 
Automatic Scaling No 
Build Automation No 
Container Health Management No 
Container Storage No 
Deployment Automation No 
File Isolation No 
Hybrid Deployments No 
Network Isolation No 
Orchestration No 
Shared File Systems No 
Version Control No 
Virtualization No 

Product Features

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Alternatives

Alternatives