
Deepchecks Hub
Deepchecks Hub enables ML practitioners to continuously validate and monitor their models and data throughout various phases of their lifecycle.
Key Capabilities of Deepchecks Hub:
ML Validation Continuity from Research to Production
You can use the exact set (or a subset) of the checks used during research for CI/CD and production monitoring. That ensures that your data science team's deep knowledge will be used by the ML Engineers in later model/data lifecycle phases.
Code-Level Root Cause Analysis
You can segment the data to view the area where the model/data seem to fail and then hand that to the data science team for code-level analysis. This results in quicker root cause analysis cycles (up to 70% of the time is usually spent on the initial analysis, which is saved here).
Scalability
Deepchecks Hub is built to handle many models, each containing a significant amount of data. Using our SDK, you can set and control numerous models, and the data each of them uses.
Our Approach
Built with Context
Deepchecks Hub works in the context of your ML, DevOps, and IT solutions. We achieved it by building our own system on top of a rich SDK and a webhook for alerts. This enables you to send data, configure monitors, alert rules, and trigger alerts in any 3rd party system.
Apply for InviteBuilt for Scale
Our architecture uses scalable building blocks, which grow automatically with data usage growth.
Real-Time
When you have a production issue, you need to be notified ASAP, and this is exactly what Deepchecks does for you. Deepchecks tracks your model metrics in real-time and raise alerts as they happen.
Secure
Single Sign On
Using “social login” (via google) or your enterprise SSO (Via SAML)
Data Privacy
Keeping your data encrypted in transit and at rest
Data Separation
A multi-layered architecture ensures that customers' data is kept separate
Secure SDK
Gives you the capability to extend our system in a secure way