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Description
Enhance machine learning model performance by capturing real-time training metrics and issuing alerts for any detected anomalies. To minimize both time and expenses associated with the training of ML models, the training processes can be automatically halted upon reaching the desired accuracy. Furthermore, continuous monitoring and profiling of system resource usage can trigger alerts when bottlenecks arise, leading to better resource management. The Amazon SageMaker Debugger significantly cuts down troubleshooting time during training, reducing it from days to mere minutes by automatically identifying and notifying users about common training issues, such as excessively large or small gradient values. Users can access alerts through Amazon SageMaker Studio or set them up via Amazon CloudWatch. Moreover, the SageMaker Debugger SDK further enhances model monitoring by allowing for the automatic detection of novel categories of model-specific errors, including issues related to data sampling, hyperparameter settings, and out-of-range values. This comprehensive approach not only streamlines the training process but also ensures that models are optimized for efficiency and accuracy.
Description
Real-time application debugging is made possible through Google Cloud's Cloud Debugger, which allows developers to examine the current state of an application without the need to pause or hinder its performance. This means that users remain unaffected while you gather information about the call stack and variables at any point in your source code. By utilizing this feature, you can gain insights into how your application behaves in a live environment, enabling you to pinpoint elusive bugs and enhance overall code quality. Furthermore, the ability to analyze live application states can greatly streamline the troubleshooting process, making it easier to maintain robust software.
API Access
Has API
API Access
Has API
Integrations
AWS Lambda
Amazon CloudWatch
Amazon SageMaker
Amazon SageMaker Studio
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
Change Healthcare Data & Analytics
Google Cloud Platform
Keras
MXNet
Integrations
AWS Lambda
Amazon CloudWatch
Amazon SageMaker
Amazon SageMaker Studio
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
Change Healthcare Data & Analytics
Google Cloud Platform
Keras
MXNet
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
1994
Country
United States
Website
aws.amazon.com/sagemaker/debugger/
Vendor Details
Company Name
Founded
1998
Country
United States
Website
cloud.google.com/debugger
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization
Product Features
Bug Tracking
Backlog Management
Filtering
Issue Tracking
Release Management
Task Management
Ticket Management
Workflow Management