Powerful Decision

What is Deepchecks Pro

You probably know that once your models pass the ‘notebook’ phase and are deployed to production, the potential business impact of undetected glitches is much greater. While weighing your options to satisfy the need for continuous validation, take a few minutes to get to know Deepchecks Pro.

Continuous Validation with Deepchecks Pro

Built on top of Deepchecks Open Source, Deepchecks Pro offers continuous machine learning model validation throughout deployment, monitoring and retraining.

Training

Inspect the model together with the data used for training, validation and testing. This can typically be done in the “notebook” environment with no need for production data.

Production

Check to see if the production data differs from the training data or changes over time. This is typically done in the production environment, but relies on aggregated data from the training phase.

New version releases

Check to see if a new “challenger” model performs better than the last version, or if new unexpected behavior is introduced. This can be done by comparing model to model or data to data (train & prod data both work).

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Same Fundamentals as the OSS Package

Like the OSS package, Deepchecks Pro is based on checks & suites related to the leading issues to observe in production. Book a demo to see how you can use the foundations of the open source package to easily configure production metrics and alerts for each new ML task you begin to monitor.

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Same Fundamentals as the OSS Package

Holistic Approach

Deepchecks Pro is an extensive MLOps solutions for enterprises and tech companies, built with multiple users in mind. Schedule a demo to see how different users can derive value from the solution while interacting with it using methods that fit naturally into their workflow.

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Holistic Approach

What Else You’ll Be Getting

Deepchecks Pro adds features critical for the production setting:

User interface and Pro dashboards
Integrations
User management
Third party alerts
Engineering support
SLA commitments
Liability, and more.

What Else You’ll Be Getting

Build vs Buy

If you’re familiar with Deepchecks Open Source, you know you could try and create your own monitoring system in-house. For ML teams solving observability challenges, there are two reasons why the best practice is to partner with a third party such as Deepchecks:

Costs

Building an ML Monitoring system in-house typically costs a lot more than initial estimations (in terms of engineering hours).

Keeping up

We’re continuously advancing Deepchecks Pro with features, dashboards and integrations so your team stays ahead seamlessly instead of falling behind on homegrown monitoring dashboards.