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WhyLabs AI Observability Platform
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WhyLabs AI Observability Platform

WhyLabs AI Observability Platform is a versatile tool that works with any cloud, giving MLOps teams powerful capabilities through its advanced model and data monitoring. It can kee..
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Freemium version
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Product Information

Description

WhyLabs AI Observability Platform is a versatile tool that works with any cloud, giving MLOps teams powerful capabilities through its advanced model and data monitoring. It can keep an eye on all kinds of datasets, no matter how big, making it easy to quickly spot problems or unusual behavior in your data and machine learning (ML) systems. This helps you keep improving things and avoid costly issues.

How to use

To get started with the WhyLabs AI Observability Platform, you simply connect its custom-designed agents to your existing data pipelines and multi-cloud systems. The platform integrates securely because these built-in agents analyze your raw data right where it is, without moving or duplicating it – ensuring your data stays private and secure. This setup lets you constantly monitor your predictive models, generative models, data pipelines, and feature stores. Plus, it can even monitor both structured and unstructured data by running 'whylogs' on your datasets and uploading the resulting logs to the platform.

Useful cases

For Logistics & Manufacturing, ensure AI consistently gives businesses a competitive edge; in Healthcare, monitor AI systems to guarantee reliability, compliance, and patient safety; for Financial Services, safeguard businesses from the risks of AI bias and opaqueness; and for Retail & E-commerce, optimize business decisions and ensure models are accurate and reliable.

Core features

  • Proactively resolve data quality issues in feature pipelines and stores; Ensure SOC 2 Type 2 security compliance; Use root cause analysis tools for issue investigation; Continuously monitor for model input and output drift; Leverage powerful monitoring algorithms for intelligent baseline and seasonal checks; Monitor model and data health; Protect against OWASP Top 10 vulnerabilities, like prompt injections and data leakage; Continuously evaluate LLM prompts and responses to ensure a positive user experience; Utilize a hybrid SaaS deployment model for highly confidential models; Benefit from enterprise-grade features, including RBAC, SAML SSO, API controls, and advanced trigger and notification configurations; Enhance LLM security for self-hosted and proprietary LLM APIs; Improve AI performance by identifying the best model candidate and reliable features; Trace cohorts that contribute to model performance and introduce bias; Take inline actions to protect against prompts with malicious intent and abuse risk; And identify training-serving skew.
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