mlapp 0.1.2

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.

• Versatile: Applicable towards a wide variety of use cases including statistical modeling, machine learning, deep learning and even optimization. Developers can install and use their favorite Python packages (scikit-learn, xgboost etc.) easily as part of their MLApp projects.
• Project scaffolding: Generates opinionated file structure that enforces modern engineering standards and improves readability across solutions.
• Embedded with MLOps: Standardizes 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.

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

configs = [
    {
        'config_path': "assets/basic_regression/configs/basic_regression_train_config.py",
        'asset_name': "basic_regression",
        'config_name': "basic_regression_config"
    }
]

Execute the run.py script:

python run.py

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.

Contributing

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