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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

Bugsnag provides comprehensive monitoring of application stability, empowering teams to make informed choices about whether to prioritize the development of new features or to address existing bugs. As a robust full-stack stability monitoring solution tailored for mobile applications, it offers advanced diagnostics that enable you to replicate any error effectively. With a user-friendly interface, you can manage all your applications seamlessly from a single dashboard. Bugsnag serves as a crucial metric for assessing app health, facilitating communication between product and engineering teams. Not every bug requires immediate attention, allowing you to concentrate on those that significantly impact your business. Its extensible libraries come with well-considered defaults and a plethora of customization options. Additionally, the team comprises subject matter experts who are genuinely invested in minimizing errors and ensuring the overall health of your applications, making Bugsnag an invaluable asset for developers.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amazon Web Services (AWS) Yes 
Amazon CloudWatch Yes 
Amazon SageMaker Studio Yes 
Azure DevOps Server No 
Bugzilla No 
Change Healthcare Data & Analytics Yes 
Codebase No 
DeployBot No 
GitLab No 
Jira Service Management No 
Okta No 
OneLogin No 
Opsgenie No 
Squadcast No 
Trello No 
Trigger No 
Twilio No 
UserVoice No 
YouTrack No 

Integrations

Amazon Web Services (AWS) Yes 
Amazon CloudWatch No 
Amazon SageMaker Studio No 
Azure DevOps Server Yes 
Bugzilla Yes 
Change Healthcare Data & Analytics No 
Codebase Yes 
DeployBot Yes 
GitLab Yes 
Jira Service Management Yes 
Okta Yes 
OneLogin Yes 
Opsgenie Yes 
Squadcast Yes 
Trello Yes 
Trigger Yes 
Twilio Yes 
UserVoice Yes 
YouTrack Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

$59 per month
Billed annually
Free Trial Yes 
Free Version Yes 

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) Yes 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person No 

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/sagemaker/debugger/

Vendor Details

Company Name

Bugsnag

Founded

2013

Country

United States

Website

www.bugsnag.com

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

Application Performance Monitoring (APM)

Baseline Manager No 
Diagnostic Tools No 
Full Transaction Diagnostics No 
Performance Control No 
Resource Management No 
Root-Cause Diagnosis No 
Server Performance No 
Trace Individual Transactions No 

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