Metadata-Version: 2.1
Name: feast
Version: 0.5.0rc0
Summary: Python SDK for Feast
Home-page: https://github.com/feast-dev/feast
Author: Feast
License: Apache
Description: # Feast - Feature Store for Machine Learning
        
        [![Unit Tests](https://github.com/feast-dev/feast/workflows/unit%20tests/badge.svg?branch=master)](https://github.com/feast-dev/feast/actions?query=workflow%3A%22unit+tests%22+branch%3Amaster)
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        ## Overview
        
        Feast (Feature Store) is a tool for managing and serving machine learning features. Feast is the bridge between models and data.
        
        Feast aims to:
        * Provide a unified means of managing feature data from a single person to large enterprises.
        * Provide scalable and performant access to feature data when training and serving models.
        * Provide consistent and point-in-time correct access to feature data.
        * Enable discovery, documentation, and insights into your features.
        
        ![](docs/.gitbook/assets/feast-docs-overview-diagram-2.svg)
        
        TL;DR: Feast decouples feature engineering from feature usage. Features that are added to Feast become available immediately for training and serving. Models can retrieve the same features used in training from a low latency online store in production.
        This means that new ML projects start with a process of feature selection from a catalog instead of having to do feature engineering from scratch.
        
        ```
        # Setting things up
        fs = feast.Client('feast.example.com')
        customer_features = ['CreditScore', 'Balance', 'Age', 'NumOfProducts', 'IsActive']
        
        # Training your model (typically from a notebook or pipeline)
        data = fs.get_batch_features(customer_features, customer_entities)
        my_model = ml.fit(data)
        
        # Serving predictions (when serving the model in production)
        prediction = my_model.predict(fs.get_online_features(customer_features, customer_entities))
        ```
        
        ## Getting Started with Docker Compose
        The following commands will start Feast in online-only mode. 
        ```
        git clone https://github.com/feast-dev/feast.git
        cd feast/infra/docker-compose
        cp .env.sample .env
        docker-compose -f docker-compose.yml -f docker-compose.online.yml up -d
        ```
        
        This will start a local Feast deployment with online serving. Additionally, a [Jupyter Notebook](http://localhost:8888/tree/feast/examples) with Feast examples.
        
        Please see the links below to set up Feast for batch/historical serving with BigQuery.
        
        ## Important resources
        
        Please refer to the official documentation at <https://docs.feast.dev>
        
         * [Why Feast?](https://docs.feast.dev/introduction/why-feast)
         * [Concepts](https://docs.feast.dev/concepts/concepts)
         * [Installation](https://docs.feast.dev/installation/overview)
         * [Examples](https://github.com/feast-dev/feast/blob/master/examples/)
         * [Roadmap](https://docs.feast.dev/roadmap)
         * [Change Log](https://github.com/feast-dev/feast/blob/master/CHANGELOG.md)
         * [Slack (#Feast)](https://join.slack.com/t/kubeflow/shared_invite/zt-cpr020z4-PfcAue_2nw67~iIDy7maAQ)
        
        ## Notice
        
        Feast is a community project and is still under active development. Your feedback and contributions are important to us. Please have a look at our [contributing guide](docs/contributing/contributing.md) for details.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
Provides-Extra: dev
