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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 Edge supports the comprehensive lifecycle of models by simplifying the management, development, validation, and governance of your entire portfolio, including AI, all from a single platform. By streamlining operations, Model Edge enhances your confidence in the program through essential tools that demonstrate model effectiveness and explainability to both internal and external stakeholders. The platform features robust model recording and documentation capabilities within a unified environment. Additionally, it offers a complete inventory of models along with an audit trail that monitors historical and real-time modifications and updates. Utilize a centralized cloud-based environment to oversee every phase of a model’s lifecycle, from its initial conception to full implementation. You can effectively manage your workflows for model development and validation while also monitoring progress across various programs, ensuring that each step is tracked and optimized. This comprehensive approach not only fosters better collaboration but also enhances accountability throughout the model management process.
API Access
Has API
API Access
Has API
Integrations
Amazon SageMaker
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
Integrations
Amazon SageMaker
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Amazon
Founded
2006
Country
United States
Website
aws.amazon.com/sagemaker/edge/
Vendor Details
Company Name
PwC
Founded
1998
Country
United Kingdom
Website
www.pwc.com/us/en/products/model-edge.html
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization
Product Features
Artificial Intelligence
Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)