sklearn-predict Run predictions inside the database.

It aims to transcompile scikit-learn models to SQL equivalents in a similar manner to tidypredict.

Often when we're building Machine Learning models, the challenges are around how can these models be built and deployed? How can we make these models smarter?

In many systems the complexity of the algorithms are often limited - this could be due to the languages or libraries which are used, the latency or complexity requirements which are in place, or other contraints.

In this package, we aim to provide a minimal set of tools to enable machine learning deployment across wide variety of contexts. Whereas packages like sklearn-porter aim to support the final classifier, we aim to provide opinionated feature engineering tools to support more complex workflows.

To provide a pathway for Python users to deploy onto databases; through enabling simple rule-based features and linear models

We do not aim to employ a range of complex mathematical transformations in our implementation. For testing, we will leverage sqlite database as the limit of the allowable in-built transformations for our machine learning pipelines.

The reasoning is that many more complex systems which we may aim to embed will likewise only support a small subset of functions. We believe that these systems too should be enabled to deployed more complex machine learning workflows in spite of such limitations

    pytest --capture=sys
    pytest --cov=sklearn_predict

• Convert the required code to use SQlAlchemy ORM better (still undecided)
• Provide end to end examples