Infrastructure-agnostic AI monitoring and operations.
Any data type at any scale.
Set up in minutes
Provision the platform using whylogs, our lightweight open-source library. Integrate with Python, Java, or Spark in a few lines of code.
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.
Amplify AI
operations in
four steps
Instrument your pipeline with whylogs
Simply install our lean, open-source library, which seamlessly integrates with on-premise infrastructure and all major cloud services.
- For python pipelines, use whylogs Python
- For Java/Spark pipelines, use whylogs Java
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Get real-time insights
Upon installation, the WhyLabs user interface immediately starts surfacing insights enabling users to:
- Analyze input data and model outputs in real-time
- Investigate how model features evolve over time
- Root-cause and fix model performance decay
Monitor for data quality and drift
Enable one-click monitoring on all model features and predictions:
- Catch data quality issues, data drifts and concept drifts
- Choose the most suitable monitoring baseline for each model
- Take action with timely alerts and notifications
Collaborate with the right people
Insights can be easily shared and used for collaboration with the right stakeholders
- Discover and share insights with fellow data scientists, ML engineers and managers
- Set up a workspace in minutes. Plug notifications into existing workflows via Slack, email or PagerDuty.
Run AI with Certainty
We take the pain out of model and data monitoring so that you spend less time
firefighting, and more time building models.