About

This section provides the API documentation for the kxi.ml Python library provided by KX for use by customers of kdb Insights, both for interactions with the Microservices/Enterprise, and independent use.

Presently this library provides users with the ability to interact with the ML Registry for persistence, retrieval and removal of Machine Learning models written in q or Python within the following libraries:

• q functions generated using PyKX.

• Scikit-learn

• Keras

• PyTorch

Installation

Ensure you have a recent version of pip:

pip install --upgrade pip

Then to install kxi.ml using the KX PyPI server run the following command:

pip install --extra-index-url=https://$KX_PYPI_USER:$KX_PYPI_PASS@nexus.dl.kx.com/repository/kxi/simple/ kxi.ml

Where the environment variables KX_PYPI_USER and KX_PYPI_PASS have been set as appropriate. Please contact kxpy@kx.com for information on getting access to the KX PyPI server.

Python Dependencies

kxi.ml depends on the following Python packages:

• kxi==1.0.2

• scipy==1.7.3

• scikit-learn==1.0.2

• statsmodels==0.13.1

• pandas==1.4.1

• joblib==1.1.0

These are installed automatically by pip when kxi.ml is installed. The following are optional dependencies which can be installed by a user to provided additional functionality:

Use cases

There are many ways users can make use of the machine learning functionality provided here:

• Train models on historical data and deploy these to real-time systems

  • Anomaly detection within manufacturing.

  • Trade surveillance.

• Machine Learning Operations development cycle

  • Storage of frequently updated models for future deployment (trading models/recommendation engines).

  • Deployment of stored models to production settings.

  • Use of versioning to maintain control over model accountability.