Deepchecks Hub

Deepchecks Hub enables ML practitioners to continuously validate and monitor their models and data throughout various phases of their lifecycle.

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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.

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Deepchecks Hub

Model performance is a critical component of a healthy application. To maximize your business performance, ML and IT teams need to continuously know their models' status. Once there is an alert, time to resolve is critical. Deepchecks Hub will help you exactly with this: identify the problematic area of your data and then quickly switch to your Python environment to find the detailed root.

Deepchecks Hub is built on our Deepchecks Open Source testing package. This allows you to customize your monitors and add custom monitors and scorers with Python code.

automatically detect model and data issues

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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.

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Built 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.