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---
layout: home-dev
---
<main role="main" class="main home">
<div class="jumbotron">
<div class="image-container">
<img class="jumbotron-img" src="{{ site.baseurl }}/assets/img/logos/datahub_color_vert-gry-bg.png"
alt="DATA HUB ICON">
</div>
<div class="title-text-container">
<h1>A Data & AI Platform for the Hybrid Cloud</h1>
</div>
<div class="button-container">
<a class="btn btn-lg btn-primary btn-getting-started" target="_blank"
href="{{ site.repo }}"
role="button">Get started
</a>
</div>
<div id="particles" class="particles"></div>
</div>
<div class="container what-is">
<div class="row dh-info">
<div class="dh-info-text-container offset-md-1 col-md-10">
<div class="dh-info-section">
<h2 class="dh-info-title">What is Open Data Hub?</h2>
<p>
The Open Data Hub is a machine-learning-as-a-service platform built on Red Hat's <a href="https://kubernetes.io/">Kubernetes</a>-based
<a href="https://www.openshift.com/">OpenShift® Container Platform</a>,
<a href="https://www.redhat.com/en/technologies/storage/ceph">Ceph Object Storage</a>, and
<a href="https://developers.redhat.com/blog/2018/05/07/announcing-amq-streams-apache-kafka-on-openshift/">Kafka/Strimzi</a>
integrating a collection of open source projects. It inherits from upstream efforts
such as <a href="https://www.kubeflow.org/">Kubeflow</a> and is the base of Red Hat's internal data-science and ML service.
Data scientists can create models using Jupyter notebooks, and select from popular tools such as
<a href="https://www.tensorflow.org/">TensorFlow™</a>, <a href="http://scikit-learn.org/stable/index.html">scikit-learn</a>,
<a href="http://spark.apache.org/">Apache Spark™</a> and more for developing models.
Teams can spend more time solving critical business needs and less on installing and maintaining infrastructure with the Open Data Hub.
</p>
<p>
Open Data Hub is a meta-project that integrates open source projects into a practical solution.
It aims to foster collaboration between communities, vendors, user-enterprises, and academics
following open source best practices. The <strong>open source community</strong> can
experiment and develop <strong>intelligent applications</strong> without incurring high costs and
having to master the complexity of modern machine learning and artificial intelligence software stacks.
</p>
<p>
Read about the new features coming to Open Data Hub in our <a href="https://opendatahub.io/news/2019-04-29/project-road-map-for-2019.html">Project Road Map</a>.
</p>
</div>
</div>
</div>
</div>
<div class="full-width-image">
<img src="{{ site.baseurl }}/assets/img/datahub_flow.png" alt="Data Hub Parts">
</div>
<div class="container getting-started">
<div class="row dh-info">
<div class="dh-info-text-container offset-md-1 col-md-10">
<div class="dh-info-section">
<h2 class="dh-info-title">Getting Started</h2>
<p>
For additional information about the Open Data Hub, <a href="{{ site.baseurl }}/news.html">read
our blogs</a>.
</p>
<p>
To set up the Open Data Hub, all you need is a running <a href="https://www.openshift.com/">OpenShift®</a>
cluster. For storing data and models, we recommend using a S3 object store such as
<a href="https://ceph.com/ceph-storage/object-storage/">Ceph</a>.
Once your OpenShift and Ceph installations are running, deploy the Open Data Hub components
using our <a href="https://www.ansible.com/">Ansible</a> playbooks and OpenShift® deployment
templates.
</p>
</div>
<div class="dh-info-section">
<p>
<a class="btn" role="button" target="_blank" href="{{ site.repo }}">
Installation
</a>
</p>
</div>
</div>
</div>
</div>
</main>