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Average Ratings 0 Ratings
Description
Amazon SageMaker Clarify offers machine learning (ML) practitioners specialized tools designed to enhance their understanding of ML training datasets and models. It identifies and quantifies potential biases through various metrics, enabling developers to tackle these biases and clarify model outputs. Bias detection can occur at different stages, including during data preparation, post-model training, and in the deployed model itself. For example, users can assess age-related bias in both their datasets and the resulting models, receiving comprehensive reports that detail various bias types. In addition, SageMaker Clarify provides feature importance scores that elucidate the factors influencing model predictions and can generate explainability reports either in bulk or in real-time via online explainability. These reports are valuable for supporting presentations to customers or internal stakeholders, as well as for pinpointing possible concerns with the model's performance. Furthermore, the ability to continuously monitor and assess model behavior ensures that developers can maintain high standards of fairness and transparency in their machine learning applications.
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
API Access
Has API
Integrations
Amazon SageMaker
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
AWS Lambda
Amazon CloudWatch
Amazon SageMaker Studio
Change Healthcare Data & Analytics
Keras
MXNet
PyTorch
Integrations
Amazon SageMaker
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
AWS Lambda
Amazon CloudWatch
Amazon SageMaker Studio
Change Healthcare Data & Analytics
Keras
MXNet
PyTorch
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/clarify/
Vendor Details
Company Name
Amazon
Founded
1994
Country
United States
Website
aws.amazon.com/sagemaker/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
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization