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Deepchecks

Meet Deepchecks Open Source

Deepchecks Open Source is a python library for data scientists and ML engineers. The package includes extensive test suites for machine learning models and data, built in a way that’s flexible, extendable and editable.

Deepchecks Deepchecks

How Does It Work?

Suites are composed of checks. Each check contains outputs to display in a notebook and/or conditions with a pass/fail output.

Conditions can be added or removed from a check;

Checks can be edited or added/removed to a suite;

Suites can be created from scratch or forked from an existing suite.


As for the “Report” - it starts with a list of the Conditions (and which passed or not) and then continues with the Display Data (from the various checks).
How Does It Work?
As for the “Report” - it starts with a list of the Conditions (and which passed or not) and then continues with the Display Data (from the various checks).

Key Features & Checks


Suites of Checks

General Suite
suite  = full_suite() result  = suite.run(train_dataset=ds_train, test_dataset=ds_test, model=rf_clf)
General Suite

Data Integrity

Data Integrity
check  = StringMismatch() result  = check.run(df)
Data Integrity

Methodology Issues

Methodology Issues
check  = BoostingOverfit() result  = check.run(train_ds, validation_ds, clf)
Methodology Issues

Distribution Checks

Distribution Checks
check  = TrainTestDrift() result  = check.run(train_dataset=train_dataset, test_dataset=test_dataset, model=model)
Distribution Checks

Performance Checks

Performance Checks
check  = SegmentPerformance(feature_1='work', feature_2='hours-per-week') Result  = check.run(validation_ds, model)
Performance Checks

Model Explainability Checks

Model Explainability Checks
Model Explainability Checks

Open Source & Community

Deepchecks is committed to keeping the ML validation package open-sourced and community-focused, so that during the training phase users will have the best possible experience. Our business model is based on charging for those extra features that are typically important for models deployed in production or relevant for internal business applications.

Recent Blog Posts

How to Automate Data Drift Thresholding in Machine Learning
How to Automate Data Drift Thresholding in Machine Learning
Best Practices for Computer Vision Model Deployment
Best Practices for Computer Vision Model Deployment
Benefits of MLOps Tools for ML Data
Benefits of MLOps Tools for ML Data

Want to Learn More About Deepchecks Pro?

Deepchecks is a solution for continuously validating ML models and data throughout the different phases of their life cycles:
1

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.

2

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.

3

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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