mlapp 0.1.1

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.

MLApp ·

MLApp is a Python library for building machine learning and AI solutions that are consistent, integrated and production-ready.

• Project scaffolding: Generates opinionated file structure that enforces modern engineering standards and improves readability across solutions
• Embedded with MLOps: Standardize the way models and their metadatas are registered, stored and deployed
• Asset boilerplates: Pre-built model templates that can be easily customized to accelerate development of common use cases
• Data science utilities: Extendable set of utilities (feature selection, autoML and other areas) increasing developer productivity
• Connectors: Easily connect to common data and analytics services
• Deployment integration: Applications built using MLApp can easily be deployed on platforms such as Kubernetes, Azure Machine Learning and others

Installation and setup

Install MLApp via pip:

pip install mlapp

Navigate to an empty project folder and generate the project scaffold:

mlapp init

Install a working example using boilerplates:

mlapp boilerplates install crash_course

Next Steps

Check out the project documentation.

A great place to start is the MLApp crash course.

Contributing to MLApp

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