Infrastructure-agnostic AI monitoring and operations. Any data type at any scale.
Easy to set up
Provision the platform using whylogs, our lightweight open-source library. Integrate with Python, Java, or Spark in a few lines of code.
Privacy preserving
Interoperable with any ML infrastructure and framework. Generate real-time insights in your existing data flow.
Cost efficient
Handle your large-scale data, keeping compute requirements low. Integrate with either batch or streaming data pipelines.
Amplify AI operations in four steps
1
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
For Java/Spark pipelines
2
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
3
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
4
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.
