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features
design
support

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Write a Review

Description

Amazon DevOps Guru leverages machine learning technology to enhance the operational efficiency and reliability of applications. This service identifies unusual behaviors that stray from standard operational patterns, allowing teams to pinpoint potential operational errors before they impact users. By utilizing machine learning models informed by years of data from Amazon.com and AWS Operational Excellence, DevOps Guru can recognize anomalous behaviors in applications, such as spikes in latency, rising error rates, and resource constraints. Furthermore, it plays a crucial role in spotting significant errors that may lead to service disruptions. Upon detecting a critical issue, DevOps Guru promptly issues an alert and supplies a comprehensive summary of the associated anomalies, potential root causes, and contextual information regarding the timing and location of the problem, thereby facilitating quicker resolution and minimizing downtime. This proactive approach not only helps maintain service quality but also empowers teams to respond effectively to incidents.

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.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amazon Web Services (AWS) Yes 
AWS AI Services Yes 
AWS App Mesh Yes 
AWS App2Container Yes 
AWS Lambda No 
Amazon CloudWatch No 
Amazon SageMaker No 
Amazon SageMaker Studio No 
Amazon SageMaker Unified Studio No 
Change Healthcare Data & Analytics No 
Keras No 
MXNet No 
PyTorch No 
TensorFlow No 

Integrations

Amazon Web Services (AWS) Yes 
AWS AI Services No 
AWS App Mesh No 
AWS App2Container No 
AWS Lambda Yes 
Amazon CloudWatch Yes 
Amazon SageMaker Yes 
Amazon SageMaker Studio Yes 
Amazon SageMaker Unified Studio Yes 
Change Healthcare Data & Analytics Yes 
Keras Yes 
MXNet Yes 
PyTorch Yes 
TensorFlow Yes 

Pricing Details

$0.0028 per resource per hour
Free Trial No 
Free Version No 

Pricing Details

No price information available.
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 Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) Yes 
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 Yes 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/es/devops-guru/

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/sagemaker/debugger/

Product Features

DevOps

Approval Workflow No 
Dashboard No 
KPIs No 
Policy Management No 
Portfolio Management No 
Prioritization No 
Release Management No 
Timeline Management No 
Troubleshooting Reports No 

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

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 

Alternatives