<commit_before><img src="logo.png" alt="MLeap Logo" width="176" height="70" />

[![Gitter](https://badges.gitter.im/combust/mleap.svg)](https://gitter.im/combust/mleap?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
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Deploying machine learning data pipelines and algorithms should not be a time-consuming or difficult task. MLeap allows data scientists and engineers to deploy machine learning pipelines from Spark and Scikit-learn to a portable format and execution engine.

Using the MLeap execution engine and serialization format, we provide a performant, portable and easy-to-integrate production library for machine learning data pipelines and algorithms.

For portability, we build our software on the JVM and only use serialization formats that are widely-adopted.

We also provide a high level of integration with existing technologies.

Our Goal:
1. Build data pipelines and train algorithms with Spark and Scikit-Learn
2. Serialize your pipeline and algorithm to Bundle.ML
3. Use MLeap to execute your pipeline and algorithm without the Spark/Scikit dependencies

Basic examples are localed below, but you can read [Serializing a Spark ML Pipeline and Scoring with MLeap](https://github.com/combust-ml/mleap/wiki/Serializing-a-Spark-ML-Pipeline-and-Scoring-with-MLeap) to gain a full sense of what is possible.
<commit_msg>Formatting updates<commit_after><img src="logo.png" alt="MLeap Logo" width="176" height="70" />

[![Gitter](https://badges.gitter.im/combust/mleap.svg)](https://gitter.im/combust/mleap?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
[![Build Status](https://travis-ci.org/combust/mleap.svg?branch=master)](https://travis-ci.org/combust/mleap)

Deploying machine learning data pipelines and algorithms should not be a time-consuming or difficult task. MLeap allows data scientists and engineers to deploy machine learning pipelines from Spark and Scikit-learn to a portable format and execution engine.

Using the MLeap execution engine and serialization format, we provide a performant, portable and easy-to-integrate production library for machine learning data pipelines and algorithms.

For portability, we build our software on the JVM and only use serialization formats that are widely-adopted.

We also provide a high level of integration with existing technologies.

Our goals for this project are:

1. Allow Researchers/Data Scientists and Engineers to continue to build data pipelines and train algorithms with Spark and Scikit-Learn
2. Extend Spark/Scikit/TensorFlow by providing ML Pipelines serialization/deserialization to/from a common framework (Bundle.ML)
3. Use MLeap Runtime to execute your pipeline and algorithm without dependenices on Spark or Scikit (numpy, pandas, etc)

Basic examples are localed below, but you can read [Serializing a Spark ML Pipeline and Scoring with MLeap](https://github.com/combust-ml/mleap/wiki/Serializing-a-Spark-ML-Pipeline-and-Scoring-with-MLeap) to gain a full sense of what is possible.

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