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ease
features
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support

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Write a Review

Description

The SageMaker Edge Agent enables the collection of data and metadata triggered by your specifications, facilitating the retraining of current models with real-world inputs or the development of new ones. This gathered information can also serve to perform various analyses, including assessments of model drift. There are three deployment options available to cater to different needs. GGv2, which is approximately 100MB in size, serves as a fully integrated AWS IoT deployment solution. For users with limited device capabilities, a more compact built-in deployment option is offered within SageMaker Edge. Additionally, for clients who prefer to utilize their own deployment methods, we accommodate third-party solutions that can easily integrate into our user workflow. Furthermore, Amazon SageMaker Edge Manager includes a dashboard that provides insights into the performance of models deployed on each device within your fleet. This dashboard not only aids in understanding the overall health of the fleet but also assists in pinpointing models that may be underperforming, ensuring that you can take targeted actions to optimize performance. By leveraging these tools, users can enhance their machine learning operations effectively.

Description

Model Garden serves as a catalog for AI and machine-learning models within the Google Cloud AI platform. It assists teams in discovering, assessing, tailoring, and implementing Google models alongside selected offerings from partners and open-source creators. Each model page offers tailored guidance and deployment alternatives; based on the specific model, users may have the opportunity to test, use a managed API, adjust settings, or deploy the model to a cloud endpoint. Developers can navigate through the Google Cloud console, utilizing supported APIs, SDKs, and command-line tools for their tasks. This resource is specifically designed for developers, data scientists, and machine-learning teams focused on creating generative AI and various applications powered by models. The availability of models, choices for deployment, and related usage costs can differ based on the model and its configuration. Therefore, users should review the specifics of each model to ensure optimal implementation.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Amazon SageMaker Yes 
Amazon SageMaker Unified Studio Yes 
Amazon Web Services (AWS) Yes 
Gemini No 
Gemini Enterprise Agent Platform No 
Google Cloud CLI No 
Google Cloud Platform No 
Hugging Face No 

Integrations

Amazon SageMaker No 
Amazon SageMaker Unified Studio No 
Amazon Web Services (AWS) No 
Gemini Yes 
Gemini Enterprise Agent Platform Yes 
Google Cloud CLI Yes 
Google Cloud Platform Yes 
Hugging Face Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Costs depend on the selected model and its usage, plus any managed endpoint or compute resources used; applicable model and infrastructure rates vary by configuration.
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 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

2006

Country

United States

Website

aws.amazon.com/sagemaker/edge/

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

cloud.google.com/model-garden

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 

Product Features

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