Metadata-Version: 2.1
Name: mlapp
Version: 0.1.5.9
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 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 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](https://mlapp-docs.s3-web.us-south.cloud-object-storage.appdomain.cloud/crash-course/introduction).
        
        You should also check out the full [project documentation](https://mlapp-docs.s3-web.us-south.cloud-object-storage.appdomain.cloud).
        
        ## Contributing
        We welcome contributions from the community. 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: rabbitmq
Provides-Extra: minio
Provides-Extra: mysql
Provides-Extra: snowflake
Provides-Extra: azure-servicebus
Provides-Extra: kafka
Provides-Extra: kafka-kerberos
Provides-Extra: boto3
Provides-Extra: ibm-boto3
Provides-Extra: azure-storage-blob
Provides-Extra: postgres
Provides-Extra: livy
Provides-Extra: mssql
Provides-Extra: pyspark
Provides-Extra: aml
Provides-Extra: sm
Provides-Extra: cp
