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 an open-source package called 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.
The whylogs logging agent is the easiest way to enable logging, testing, and monitoring in an ML/AI application. The lightweight agent profiles data in real-time, collecting thousands of metrics from structured data, unstructured data, and ML model predictions with zero configuration.
First, install whylogs:
Then, start logging statistical properties of features, model inputs, and model outsputs to enable explorative analysis, data unit testing, and monitoring. Getting whylogs up-and-running is easy:
With whylogs integrated into your workflow, the next step is to onboard with the WhyLabs SaaS Platform to monitor model inputs, outputs, and performance. Onboarding only takes a few minutes, so please contact us to request an account.
- 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.
Brainstorm ideas and share feedback with the whylogs community members on Slack!