How Does It Work?
Deepchecks Open Source
Install Deepchecks Open Source to Take Care of Both Testing and Monitoring of Your ML
In the past, Deepchecks Open Source was focused only on testing ML during the research phase. As of June 2023, Deepchecks expanded this to a combination of testing and monitoring, so that data scientists and ML engineers can take multiple steps in their evaluation journey while remaining in the open source realm.
Deepchecks Hub
Combining the Continuous Evaluation of Multiple Models, in a Managed and Secure Setting, from Research to Production
Deepchecks Hub expands the functionality of Deepchecks Open Source to include everything your team will need in a commercial setting. This consists of a variety of scalable deployment options, a unified experience of validating multiple models in parallel, security and access management features, and support.
Deepchecks Supported Data Types
Deepchecks supports: Tabular data, Computer Vision and NLP throughout your model and
data lifecycle from training to production
data lifecycle from training to production
Explainer Video

pip install -U deepchecks
from deepchecks.tabular.suites
import train_test_validation
Run Test Suites for CI/CD
Deepchecks Open Source can be used for CI/CD by
integrating just a few lines of code to your CI/CD
scripts. This will help you to ensure that your re-trained
model will not cause issues when deployed to
production.
Book a Demointegrating just a few lines of code to your CI/CD
scripts. This will help you to ensure that your re-trained
model will not cause issues when deployed to
production.
on:
push: ['main']
job:
run_suites:
train_dataset: 'load.py:get_train_dataset'
test_dataset: 'load.py:get_test_dataset'
model: 'load.py:get_model'
suites: ['custom_suites.py:my_model_evaluation_suite']
Open Source & Community
Deepchecks is committed to keeping the ML evaluation package open-source and community-focused.