Deepchecks is a minimally intrusive MLOps solution for continuous validation of machine learning systems, meant to enable you to trust your models through the continuous changes in your data lifecycle. Deepchecks includes the must-have features for any ML Monitoring system: performance monitoring, data drift detection and anomaly detection alerts, along with some unique features that are especially helpful for complex ML pipelines: Monitoring various phases of the pipeline, detecting hidden data integrity issues, detecting low confidence segments, detecting inconsistencies that are hidden within unstructured text, etc.
Deepchecks can fit into existing pipelines within all of the major cloud platforms, as well as some on-prem/hybrid architectures.
Free trial and then ongoing costs are based on the number of models & the amount of analyzed data
To get a feel of our system, please try out our sandbox. It’s worth noting that this is a version with limited screens and functionality, the full version can be opened for experimentation upon request.
Examples of use cases can be found on our website:
We also support use cases such as AI based cyber-security systems, underwriting, asset evaluation, ad-tech (bidding, personalization, etc.), e-commerce (conversion from cart, recommender systems)
|How much does it cost?|
|What’s a sample use case? Where can I learn from?|