mlapp 0.1.0

IBM Services Framework for ML Applications Python 3 framework for building robust, production-ready machine learning applications. Official ML accelerator within the larger RAD-ML methodology.

IBM Services Framework for ML Applications (MLApp)

IBM Services Framework for ML Applications (MLApp) is a Python 3 framework for building machine learning applications that are robust and production-ready. It was developed by IBMers based on learnings from dozens of machine learning projects for IBM clients.

IBM Services Framework for ML Applications is the official RAD-ML component accelerator for analytics applications. RAD-ML is a proven methodology for developing sellable, reusable, and scalable machine learning assets.

Installation

In your terminal, execute the following command to install MLApp:

pip install mlapp

Installing Extras for MLApp

For using extra capabilities of ML App use the pip install command and add brackets with the extra you wish to install:

pip install "mlapp[mlcp]" - installs ML App with all libraries that are required for using the Machine Learning Control Panel (in-house control panel).

pip install "mlapp[aml]" - installs ML App with all libraries that are required for using Azure Machine Learning.

Note: check in setup.py the extras_require for more information on 3rd party libraries we use to connect with different services.

Initiating a New MLApp Project

You are now ready to start your MLApp project! To do so, navigate to the your new project's folder in your terminal and run the following command:

mlapp init

This command creates the required files for a MLApp project.

Next Steps

Check out the Documentation.

A great place to start is the ML App Crash Course.

Contributing to MLApp

We welcome contributions from the community to this framework. Please refer to CONTRIBUTING for more information.

Main Authors

• Tomer Galula
• Tal Waitzenberg
• Erez Nardia
• Michael Chein
• Annaelle Cohen
• Nathan Katz
• Keren Haddad-Leibovich