Metadata-Version: 2.1
Name: mlapp
Version: 0.1.1
Summary: 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.
Home-page: https://github.com/ibm/mlapp
Author: IBM
Author-email: tomer.galula@ibm.com, tal.waitzenberg@ibm.com, michael.chein@ibm.com, erez.nardia@ibm.com, annaelle.cohen@ibm.com, katzn@us.ibm.com
License: Apache License 2.0
Project-URL: Bug Reports, https://github.com/ibm/mlapp/issues
Project-URL: Wiki Page, https://mlapp-docs.s3-web.us-south.cloud-object-storage.appdomain.cloud
Project-URL: Crash Course, https://mlapp-docs.s3-web.us-south.cloud-object-storage.appdomain.cloud/crash-course/introduction
Project-URL: Source, https://github.com/ibm/mlapp
Description: 
        
        # MLApp &middot; [![pip version](https://img.shields.io/pypi/v/mlapp?color=success)](https://pypi.python.org/pypi/mlapp/) [![Build Status](https://travis-ci.com/IBM/mlapp.svg?branch=master)](https://travis-ci.com/IBM/mlapp) [![License](https://img.shields.io/badge/license-Apache-blue.svg)](https://github.com/IBM/mlapp/blob/master/LICENSE)
        
        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](https://mlapp-docs.s3-web.us-south.cloud-object-storage.appdomain.cloud).
        
        A great place to start is the [MLApp crash course](https://mlapp-docs.s3-web.us-south.cloud-object-storage.appdomain.cloud/crash-course/introduction).
        
        ## Contributing to MLApp
        We welcome contributions from the community to this framework. Please refer to [CONTRIBUTING](./CONTRIBUTING.md) for more information.
Keywords: mlapp,ibm,machine-learning,auto-ml
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: pika
Provides-Extra: minio
Provides-Extra: PyMySQL
Provides-Extra: snowflake-sqlalchemy
Provides-Extra: azure-servicebus
Provides-Extra: kafka
Provides-Extra: boto3
Provides-Extra: azure-storage-blob
Provides-Extra: postgres
Provides-Extra: livy
Provides-Extra: pyspark
Provides-Extra: aml
Provides-Extra: mlcp
