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Average Ratings 0 Ratings

Total
ease
features
design
support

No User Reviews. Be the first to provide a review:

Write a Review

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

Screenshots View All

Screenshots View All

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
PyTorch
TensorFlow

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
PyTorch
TensorFlow

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

Google

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

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