MASQ is a framework able to run inference of ML models directly on DBMSs. MASQ not only averts expensive data movements for those predictive scenarios where data resides on a database, but it also naturally exploits all the “Enterprise-grade” features such as governance, security and auditability which make DBMSs the cornerstone of many businesses. MASQ compiles trained models and ML pipelines implemented in scikit-learn directly into standard SQL: no UDFs nor vendor-specific syntax are used, and therefore queries can be readily executed on any DBMS.
Currently, you can use MASQ to convert your trained traditional ML models into SQL query. MASQ supports a variety of classifiers and regressors. These models include scikit-learn Gradient Boosting Classifier, Regressor and Logistic Regression.
Support for other models and neural network backends is on our roadmap.
Frontend GUI available at MASQ_frontend
MASQ was tested on Python >= 3.5 on Linux, Windows and MacOS machines.
It is recommended to use a virtual environment
(See: python3 venv doc).
Clone the project into local
Install the MASQ package:
$ pip install -r requirements.txtRun the Django command-line utilities to create the database tables automatically:
$ python manage.py makemigrations
$ python manage.py migrateIn order to run the application type the following command
$ python manage.py runserverThe Application Runs on localhost:8000