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What is WhyLabs?

Elevate your observability framework to quickly pinpoint challenges in data and machine learning, enabling continuous improvements while averting costly issues. Start with reliable data by persistently observing data-in-motion to identify quality problems. Effectively recognize shifts in both data and models, and acknowledge differences between training and serving datasets to facilitate timely retraining. Regularly monitor key performance indicators to detect any decline in model precision. It is essential to identify and address hazardous behaviors in generative AI applications to safeguard against data breaches and shield these systems from potential cyber threats. Encourage advancements in AI applications through user input, thorough oversight, and teamwork across various departments. By employing specialized agents, you can integrate solutions in a matter of minutes, allowing for the assessment of raw data without the necessity of relocation or duplication, thus ensuring both confidentiality and security. Leverage the WhyLabs SaaS Platform for diverse applications, utilizing a proprietary integration that preserves privacy and is secure for use in both the healthcare and banking industries, making it an adaptable option for sensitive settings. Moreover, this strategy not only optimizes workflows but also amplifies overall operational efficacy, leading to more robust system performance. In conclusion, integrating such observability measures can greatly enhance the resilience of AI applications against emerging challenges.

What is Aporia?

Create customized monitoring solutions for your machine learning models with our intuitive monitor builder, which alerts you to potential issues like concept drift, decreases in model performance, biases, and more. Aporia seamlessly integrates with any machine learning setup, be it a FastAPI server on Kubernetes, an open-source solution like MLFlow, or cloud services such as AWS Sagemaker. You can dive into specific data segments to closely evaluate model performance, enabling you to detect unexpected biases, signs of underperformance, changing features, and data integrity problems. When your machine learning models encounter difficulties in production, it's essential to have the right tools to quickly diagnose the root causes. Beyond monitoring, our investigation toolbox provides an in-depth analysis of model performance, data segments, statistical information, and distribution trends, ensuring you have a comprehensive grasp of how your models operate. This thorough methodology enhances your monitoring capabilities and equips you to sustain the reliability and precision of your machine learning solutions over time, ultimately leading to better decision-making and improved outcomes for your projects.

Media

Media

Integrations Supported

Amazon SageMaker
Flask
Python
UbiOps

Integrations Supported

Amazon SageMaker
Amazon S3
Azure Blob Storage
Google Cloud Storage
Grafana Cloud
Jira
MLflow
Microsoft Teams
New Relic
Prometheus
Sagify
Slack

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

WhyLabs

Date Founded

2019

Company Location

United States

Company Website

whylabs.ai/

Company Facts

Organization Name

Aporia

Company Website

www.aporia.com

Categories and Features

AI Development

Not specified

AI Governance

Not specified

AI Observability

Not specified

AI Security

Not specified

Machine Learning

Not specified

Observability

Not specified

Categories and Features

AI Governance

Not specified

Machine Learning

Not specified

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