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.
Project description
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.
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