How Does it Work?
Run Checks & Suites Periodically to Test &
Monitor Your ML in Production
Continuously tests your ML models in production and pre-production environments. Going beyond monitoring, it helps you understand the granular root cause. Supports cloud and managed on-prem.Apply for Invite
pip install -U deepchecks-client dc_client = DeepchecksClient(HOST, TOKEN) dc_client.create_tabular_model_version(...)
Deepchecks Open Source
Deepchecks Open Source is a python library for data scientists and ML engineers that enables you to test your models and data. Start your validation journey with testing while developing your models, training, and CI/CD.
Use predefined checks and suites, or configure your own.
Deepchecks Supported Data Types
Deepchecks supports tabular data, computer vision, and NLP throughout your model and data lifecycle from training to production.
Apply for Invite
Public Beta for Testing & CI/CD
What is Continuous ML Validation?
Easy to Install
Using Deepchecks is simple. For example, using Deepchecks Open Source for tabular data requires only two lines of code which can be used in any Python environment.
Please refer to Deepchecks Open Source “Getting Started” here or Deepchecks Hub "Getting Started" here for more information.
from deepchecks.tabular.suites import train_test_validation validation_suite = train_test_validation(...) validation_suite.run(train_ds, test_ds)
Run Test Suites for CI/CD
Deepchecks Open Source can be used for CI/CD by integrating just a few lines of code into your CI/CD scripts. This will help to ensure that your re-trained model will not cause issues when deployed to production.Book a Demo
result = my_custom_suite.run(prod_ds, model) assert result.passed()
Open Source & Community
Deepchecks is committed to keeping the ML validation package open-source and community-focused.