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What is Slim.AI?

Effortlessly integrate your own private registries while collaborating with your team by sharing images with ease. Explore the vast array of public registries to find the perfect container image suited for your specific project requirements. It's crucial to comprehend the contents of your containers to maintain software security. The Slim platform reveals the complexities of container internals, allowing you to analyze, optimize, and assess changes across different containers or versions. Utilize DockerSlim, our open-source project, to automatically simplify and improve your container images. By removing unnecessary or potentially harmful packages, you ensure that only what is essential for production is deployed. Discover how the Slim platform can help your team bolster software and supply chain security, optimize containers for various stages like development and testing, and securely launch container-based applications in the cloud. Creating an account is currently free, and the platform remains accessible without charge. As advocates for container technology rather than mere sales representatives, we place a strong emphasis on privacy and security, which are the foundational principles of our business. Moreover, we pledge to continually refine our services in response to user input, ensuring that we meet your needs more effectively over time. This commitment to improvement reflects our dedication to you and your projects.

What is AWS Deep Learning Containers?

Deep Learning Containers are specialized Docker images that come pre-loaded and validated with the latest versions of popular deep learning frameworks. These containers enable the swift establishment of customized machine learning environments, thus removing the necessity to build and refine environments from scratch. By leveraging these pre-configured and rigorously tested Docker images, users can set up deep learning environments in a matter of minutes. In addition, they allow for the seamless development of tailored machine learning workflows for various tasks such as training, validation, and deployment, integrating effortlessly with platforms like Amazon SageMaker, Amazon EKS, and Amazon ECS. This simplification of the process significantly boosts both productivity and efficiency for data scientists and developers, ultimately fostering a more innovative atmosphere in the field of machine learning. As a result, teams can focus more on research and development instead of getting bogged down by environment setup.

Media

Media

Integrations Supported

Amazon Elastic Container Registry (ECR)
AWS Marketplace
AWS Neuron
Amazon EC2 G5 Instances
Amazon EC2 P4 Instances
Amazon EC2 P5 Instances
Amazon EC2 Trn1 Instances
Amazon EKS
Amazon Elastic Container Service (Amazon ECS)
Amazon SageMaker
Amazon Web Services (AWS)
Docker
Google Cloud Container Registry
Red Hat Quay

Integrations Supported

Amazon Elastic Container Registry (ECR)
AWS Marketplace
AWS Neuron
Amazon EC2 G5 Instances
Amazon EC2 P4 Instances
Amazon EC2 P5 Instances
Amazon EC2 Trn1 Instances
Amazon EKS
Amazon Elastic Container Service (Amazon ECS)
Amazon SageMaker
Amazon Web Services (AWS)
Docker
Google Cloud Container Registry
Red Hat Quay

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Slim.AI

Date Founded

2020

Company Website

www.slim.ai/

Company Facts

Organization Name

Amazon

Date Founded

2006

Company Location

United States

Company Website

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

Categories and Features

Container Management

Access Control
Application Development
Automatic Scaling
Build Automation
Container Health Management
Container Storage
Deployment Automation
File Isolation
Hybrid Deployments
Network Isolation
Orchestration
Shared File Systems
Version Control
Virtualization

Categories and Features

Container Management

Access Control
Application Development
Automatic Scaling
Build Automation
Container Health Management
Container Storage
Deployment Automation
File Isolation
Hybrid Deployments
Network Isolation
Orchestration
Shared File Systems
Version Control
Virtualization

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