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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
Amazon SageMaker JumpStart serves as a comprehensive hub for machine learning (ML), designed to expedite your ML development process. This platform allows users to utilize various built-in algorithms accompanied by pretrained models sourced from model repositories, as well as foundational models that facilitate tasks like article summarization and image creation. Furthermore, it offers ready-made solutions aimed at addressing prevalent use cases in the field. Additionally, users have the ability to share ML artifacts, such as models and notebooks, within their organization to streamline the process of building and deploying ML models. SageMaker JumpStart boasts an extensive selection of hundreds of built-in algorithms paired with pretrained models from well-known hubs like TensorFlow Hub, PyTorch Hub, HuggingFace, and MxNet GluonCV. Furthermore, the SageMaker Python SDK allows for easy access to these built-in algorithms, which cater to various common ML functions, including data classification across images, text, and tabular data, as well as conducting sentiment analysis. This diverse range of features ensures that users have the necessary tools to effectively tackle their unique ML challenges.
API Access
Has API
API Access
Has API
Integrations
Amazon SageMaker
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
Integrations
Amazon SageMaker
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
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
2006
Country
United States
Website
aws.amazon.com/sagemaker/jumpstart/
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