docs/gitbook/README.md - incubator-hivemall - Git at Google

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 # Introduction

 <div class="alert alert-info">
 Apache Hivemall is a collection of machine learning algorithms and versatile data analytics functions. It provides a number of ease of use machine learning functionalities through the <a href="https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF">Apache Hive UDF/UDAF/UDTF interface</a>.
 </div>

 <div style="text-align:center"><img src="./resources/images/hivemall-logo-color-small.png"/></div>

 Apache Hivemall offers a variety of functionalities: <strong>regression, classification, recommendation, anomaly detection, k-nearest neighbor, and feature engineering</strong>. It also supports state-of-the-art machine learning algorithms such as Soft Confidence Weighted, Adaptive Regularization of Weight Vectors, Factorization Machines, and AdaDelta.

 ## Architecture

 Apache Hivemall is mainly designed to run on [Apache Hive](https://hive.apache.org/) but it also supports [Apache Pig](https://pig.apache.org/) and [Apache Spark](https://spark.apache.org/) for the runtime.
 Thus, it can be considered as a cross platform library for machine learning; prediction models built by a batch query of Apache Hive can be used on Apache Spark/Pig, and conversely, prediction models build by Apache Spark can be used from Apache Hive/Pig.

 <div style="text-align:center"><img src="./resources/images/techstack.png" width="80%" height="80%"/></div>
	<!--
	Licensed to the Apache Software Foundation (ASF) under one
	or more contributor license agreements. See the NOTICE file
	distributed with this work for additional information
	regarding copyright ownership. The ASF licenses this file
	to you under the Apache License, Version 2.0 (the
	"License"); you may not use this file except in compliance
	with the License. You may obtain a copy of the License at

	http://www.apache.org/licenses/LICENSE-2.0

	Unless required by applicable law or agreed to in writing,
	software distributed under the License is distributed on an
	"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
	KIND, either express or implied. See the License for the
	specific language governing permissions and limitations
	under the License.
	-->

	# Introduction

	<div class="alert alert-info">
	Apache Hivemall is a collection of machine learning algorithms and versatile data analytics functions. It provides a number of ease of use machine learning functionalities through the <a href="https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF">Apache Hive UDF/UDAF/UDTF interface</a>.
	</div>

	<div style="text-align:center"><img src="./resources/images/hivemall-logo-color-small.png"/></div>

	Apache Hivemall offers a variety of functionalities: <strong>regression, classification, recommendation, anomaly detection, k-nearest neighbor, and feature engineering</strong>. It also supports state-of-the-art machine learning algorithms such as Soft Confidence Weighted, Adaptive Regularization of Weight Vectors, Factorization Machines, and AdaDelta.

	## Architecture

	Apache Hivemall is mainly designed to run on [Apache Hive](https://hive.apache.org/) but it also supports [Apache Pig](https://pig.apache.org/) and [Apache Spark](https://spark.apache.org/) for the runtime.
	Thus, it can be considered as a cross platform library for machine learning; prediction models built by a batch query of Apache Hive can be used on Apache Spark/Pig, and conversely, prediction models build by Apache Spark can be used from Apache Hive/Pig.

	<div style="text-align:center"><img src="./resources/images/techstack.png" width="80%" height="80%"/></div>