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What is Amazon SageMaker Edge?

The SageMaker Edge Agent is designed to gather both data and metadata according to your specified parameters, which supports the retraining of existing models with real-world data or the creation of entirely new models. The information collected can also be used for various analytical purposes, such as evaluating model drift. There are three different deployment options to choose from. One option is GGv2, which is about 100MB and offers a fully integrated solution within AWS IoT. For those using devices with constrained capabilities, we provide a more compact deployment option built into SageMaker Edge. Additionally, we support clients who wish to utilize alternative deployment methods by permitting the integration of third-party solutions into our workflow. Moreover, Amazon SageMaker Edge Manager includes a dashboard that presents insights into the performance of models deployed throughout your network, allowing for a visual overview of fleet health and identifying any underperforming models. This extensive monitoring feature empowers users to make educated decisions regarding the management and upkeep of their models, ensuring optimal performance across all deployments. In essence, the combination of these tools enhances the overall effectiveness and reliability of model management strategies.

What is Amazon SageMaker Autopilot?

Amazon SageMaker Autopilot streamlines the creation of machine learning models by taking care of the intricate details on your behalf. You simply need to upload a tabular dataset and specify the target column for prediction; from there, SageMaker Autopilot methodically assesses a range of techniques to find the most suitable model. Once the best model is determined, you can easily deploy it into production with just one click, or you have the option to enhance the recommended solutions for improved performance. It also adeptly handles datasets with missing values, as it automatically fills those gaps, provides statistical insights about the dataset features, and derives useful information from non-numeric data types, such as extracting date and time details from timestamps. Moreover, the intuitive interface of this tool ensures that it is accessible not only to experienced data scientists but also to beginners who are just starting out. This makes it an ideal solution for anyone looking to leverage machine learning without needing extensive expertise.

Media

Media

Integrations Supported

Amazon SageMaker
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)

Integrations Supported

Amazon SageMaker
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)

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

Amazon

Date Founded

2006

Company Location

United States

Company Website

aws.amazon.com/sagemaker/edge/

Company Facts

Organization Name

Amazon

Date Founded

2006

Company Location

United States

Company Website

aws.amazon.com/sagemaker/autopilot

Categories and Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Categories and Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

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