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Description
Amazon SageMaker Model Monitor enables users to choose which data to observe and assess without any coding requirements. It provides a selection of data types, including prediction outputs, while also capturing relevant metadata such as timestamps, model identifiers, and endpoints, allowing for comprehensive analysis of model predictions in relation to this metadata. Users can adjust the data capture sampling rate as a percentage of total traffic, particularly beneficial for high-volume real-time predictions, with all captured data securely stored in their designated Amazon S3 bucket. Additionally, the data can be encrypted, and users have the ability to set up fine-grained security measures, establish data retention guidelines, and implement access control protocols to ensure secure data handling. Amazon SageMaker Model Monitor also includes built-in analytical capabilities, utilizing statistical rules to identify shifts in data and variations in model performance. Moreover, users have the flexibility to create custom rules and define specific thresholds for each of those rules, enhancing the monitoring process further. This level of customization allows for a tailored monitoring experience that can adapt to varying project requirements and objectives.
Description
Enhance your observability framework to swiftly identify data and machine learning challenges, facilitate ongoing enhancements, and prevent expensive incidents.
Begin with dependable data by consistently monitoring data-in-motion to catch any quality concerns. Accurately detect shifts in data and models while recognizing discrepancies between training and serving datasets, allowing for timely retraining. Continuously track essential performance metrics to uncover any decline in model accuracy. It's crucial to identify and mitigate risky behaviors in generative AI applications to prevent data leaks and protect these systems from malicious attacks. Foster improvements in AI applications through user feedback, diligent monitoring, and collaboration across teams.
With purpose-built agents, you can integrate in just minutes, allowing for the analysis of raw data without the need for movement or duplication, thereby ensuring both privacy and security. Onboard the WhyLabs SaaS Platform for a variety of use cases, utilizing a proprietary privacy-preserving integration that is security-approved for both healthcare and banking sectors, making it a versatile solution for sensitive environments. Additionally, this approach not only streamlines workflows but also enhances overall operational efficiency.
API Access
Has API
API Access
Has API
Integrations
Amazon SageMaker
Amazon Web Services (AWS)
Flask
Python
UbiOps
Integrations
Amazon SageMaker
Amazon Web Services (AWS)
Flask
Python
UbiOps
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/model-monitor/
Vendor Details
Company Name
WhyLabs
Founded
2019
Country
United States
Website
whylabs.ai/
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