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Introduction

WhyLabs is an infrastructure-agnostic AI monitoring and operations solution for any data type at any scale.

Quick start#

Install the open source logging agent - whylogs: Start logging statistical properties of features, model inputs and model outsputs to enable explorative analysis, data unit testing and monitoring.

Set up integrations with whylogs: Learn how to log and collect ML-specific metrics, traces and logs with simple integrations.

Get started in the WhyLabs app: Discover how to use WhyLabs to to monitor model inputs, outputs, and performance.

Learn more about whylogs - open source logging agent#

whylogs provides lightweight data collection, enterprise scalability, and flexibility designed for data science workflows. It has built-in data tagging and aggregation capabilities. Furthermore, the installation to take minutes and seamlessly integrate with existing tools. You can read an in-depth overview about whylogs.

Wondering if the whylogs is a good fit for your use case? Check out our use cases section or join our Slack channel.

Contribute to whylogs?#

Want to contribute? Please visit our GitHub repo to get started.

Learn more about WhyLabs - AI observability platform#

WhyLabs is an observability platform designed to monitor data pipelines and ML applications for data quality regressions, data drift and model performance degradation. Built on top of whylogs, the platform enables AI builders to:

  • Set up in minutes: provision the platform using whylogs, the lightweight open-source library.

  • Integrate seamlessly: interoperable with any ML infrastructure and framework. Generate real-time insights in your existing data flow.

  • Scale to terabytes: handle your large-scale data, keeping compute requirements low. Integrate with either batch or streaming data pipelines.

Resources#