How Does it Work?
Deepchecks Hub
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.
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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.
Vision
Public Beta for Testing & CI/CD
Tabular
NLP
Private Beta
What is Continuous ML Validation?
Easy to Install
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.
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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.
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Recent Blog Posts
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