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| title: Welcome to PredictionIO <%= data.versions.pio %> |
| description: PredictionIO Open Source Machine Learning Server |
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| ###Build and Deploy Predictive Engines |
| ####on production environments, in a fraction of the time. |
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| PredictionIO is an open source Machine Learning Server for developers and data scientists to build and deploy predictive engines. |
| PredictionIO lets you quickly deploy an engine as a web service on production by choosing one from the template gallery. |
| PredictionIO lets you customize the code of every engine components for your specific business needs. |
| PredictionIO also enables you to evaluate, and tune, multiple engine variants systematically. |
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| PredictionIO consists of: |
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| * **PredictionIO platform** - our open source machine learning stack for building, evaluating and deploying engines with machine learning algorithms. |
| * **Event Server** - our open source machine learning analytics layer for unifying events from multiple platforms |
| * **Template Gallery** - the place for you to download engine templates for different type of machine learning applications |
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| System Architecture: |
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| PredictionIO can be installed as a full machine learning stack, bundled with **Apache Spark**, **MLlib**, **HBase**, **Spray** and **Elasticsearch**, which simplifies and accelerates scalable machine learning infrastructure management. |
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| ## Why PredictionIO |
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| * Open Source |
| * Built on top of state-of-the-art open source stack: Apache Spark, HBase and Spray |
| * Build predictve engines faster with customizable templates for all kind of machine learning tasks |
| * Deploy engines as scalable web services easily |
| * Support building multiple type of engines for an application on a single platform |
| * Unify data from multiple platforms in batch or in real-time for comprehensive predictive analytics |
| * Simplify data infrastructure management |
| * Speed up machine learning modeling with systematic processes and pre-built evaluation measures |
| * Support algorithm libraries such as Spark MLlib seamlessly, also allow you to create your own |
| * Designed for distributed production deployment |
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| ## About this guide |
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| The following sections will recommended if you are new to PredictionIO: |
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| * [Getting Started](/start/) - Install PredictionIO and try to deploy an engine! |
| * [App Integration Overview](/appintegration/) - Learn how to integrate Prediction with your web/mobile app |
| * Browse the [template gallery](http://templates.prediction.io) to choose an engine template |
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| For advanced learning, you may: |
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| * [Learn the DASE architecture](/customize/) of an engine. |
| * [Understand PredictionIO architecture](/system) |
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| ## Installation |
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| The [installation section](/install/) will show you how to install PredictionIO on a variety of platforms. |
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| ## Release Notes |
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| A summary of the changes in each release in the current series can now be found on the separate [Release Notes](/release-notes) page |
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| ## Licensing |
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| PredictionIO is licensed under the Apache License, Version 2.0. See [LICENSE](https://github.com/PredictionIO/PredictionIO/blob/master/LICENSE.txt) for the full license text. |
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| ### Documentation |
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| Documentation is under a [Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License](http://creativecommons.org/licenses/by-nc-sa/3.0/). |
