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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 · pip version Build Status License

MLApp is a Python library for building scalable data science solutions that meet modern software engineering standards.

MLApp was built and hardened in an enterprise context, to solve scalability issues for mid-size to Fortune 50 companies. It is applicable to a variety of data science use cases including machine learning, deep learning, NLP and optimization.

  • Embedded MLOps: Standardizes the way models and their metadatas are registered, stored and deployed.
  • Project scaffolding: Generates an opinionated project file structure that enforces modern engineering standards and improves readability and documentation across solutions.
  • Boilerplates: Includes a library of pre-built model templates that can be easily customized to accelerate development of common use cases.
  • Utilities: Includes an extendable set of utilities that increase developer productivity - including functions for selecting features and optimizing hyperparameters.
  • Connectors: Allows developers to easily integrate their projects with common data and analytics services.
  • Deployment integration: Applications built using MLApp can easily be deployed on common open and proprietary platforms, including Kubernetes and Azure Machine Learning.

Getting started

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 basic_regression

Update the file in your project directory to point to the Basic Regression asset that you just installed:

configs = [
        'config_path': "assets/basic_regression/configs/",
        'asset_name': "basic_regression",
        'config_name': "basic_regression_config"

Execute the script:


Congrats! You've trained your first model in MLApp. Take a look at the output directory to see the results.

Next steps

A great place to start is the crash course.

You should also check out the full project documentation.


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

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