Data Quality Metrics (DQM-ML)

Documentation

Methodological Guidelines

1. Methodology for Dataset Development
2. Methological Guideline for Data Engineering

Scientific contribution

1. Characterization and Metrics for data coverage and quality

State of the art

1. State of the Art on Data Exploration and Qualification
2. State of the Art on domain shifts characterization between source annotated data and new target data
3. State of the Art on Data and Dataset Specification for AI

Examples

Some examples of applications using DQM-ML are provided with the component , you can find them in the /examples folder of the source code

Support

Support for Data Quality Metrics can be obtained by sending an email to support@confiance.ai

Ensure your email contains :

• Your name
• A link to this page
• the version you are working with
• A clear description of the problematic (bug, crash, feature or help request)
• A full description of the problem whichallow to reproduce it
• Any file or screenshort element mandatory for the full understanding of the problematic

Official package 

The package is directly downlaodable from Pypi : https://pypi.org/project/dqm-ml/1.1.0/

Local Available files

• data_quality_metrics-1.1.0.tar.gz : The installable python  package of the library.
• DataQualityMetrics-source-code-1.1.0.zip : The source code of the library.