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About

Using Amazon SageMaker Pipelines, you can create ML workflows with an easy-to-use Python SDK, and then visualize and manage your workflow using Amazon SageMaker Studio. You can be more efficient and scale faster by storing and reusing the workflow steps you create in SageMaker Pipelines. You can also get started quickly with built-in templates to build, test, register, and deploy models so you can get started with CI/CD in your ML environment quickly. Many customers have hundreds of workflows, each with a different version of the same model. With the SageMaker Pipelines model registry, you can track these versions in a central repository where it is easy to choose the right model for deployment based on your business requirements. You can use SageMaker Studio to browse and discover models, or you can access them through the SageMaker Python SDK.

About

Amazon SageMaker Studio is an integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps, from preparing data to building, training, and deploying your ML models, improving data science team productivity by up to 10x. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, collaborate seamlessly within your organization, and deploy models to production without leaving SageMaker Studio. Perform all ML development steps, from preparing raw data to deploying and monitoring ML models, with access to the most comprehensive set of tools in a single web-based visual interface. Amazon SageMaker Unified Studio is a comprehensive, AI and data development environment designed to streamline workflows and simplify the process of building and deploying machine learning models.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Individuals that need a first purpose-built CI/CD service for machine learning

Audience

Professionals interested in a tool to perform all ML development steps, from preparing data to deploying and monitoring ML models

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Amazon
Founded: 2006
United States
aws.amazon.com/sagemaker/pipelines/

Company Information

Amazon
Founded: 1994
United States
aws.amazon.com/sagemaker/studio/

Alternatives

Alternatives

Amazon SageMaker Ground Truth

Amazon SageMaker Ground Truth

Amazon Web Services

Categories

Categories

Integrations

Amazon SageMaker
Amazon Web Services (AWS)
AWS Glue
Amazon EMR
Amazon SageMaker Data Wrangler
Amazon SageMaker Debugger
Jupyter Notebook
PyTorch
TensorFlow

Integrations

Amazon SageMaker
Amazon Web Services (AWS)
AWS Glue
Amazon EMR
Amazon SageMaker Data Wrangler
Amazon SageMaker Debugger
Jupyter Notebook
PyTorch
TensorFlow
Claim Amazon SageMaker Pipelines and update features and information
Claim Amazon SageMaker Pipelines and update features and information
Claim Amazon SageMaker Studio and update features and information
Claim Amazon SageMaker Studio and update features and information