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9 changes: 6 additions & 3 deletions content/en/_index.html
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<div class="container">
<h3 class="section-head">What is Kubeflow?</h3>
<p class="mx-auto col-md-8 px-0">
The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable.
Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures.
Kubeflow makes artificial intelligence and machine learning simple, portable, and scalable.
We are an <i>ecosystem</i> of <a href="https://kubernetes.io/" target="_blank">Kubernetes</a>
based components for each stage in
<a href="/docs/started/architecture/#kubeflow-components-in-the-ml-lifecycle" target="_blank">the AI/ML Lifecycle</a>
with support for best-in-class open source <a href="/docs/started/architecture/#kubeflow-ecosystem" target="_blank">tools and frameworks</a>.
<br><br>
Anywhere you are running Kubernetes, you should be able to run Kubeflow.
<a href="/docs/started/installing-kubeflow/" target="_blank">Deploy Kubeflow</a> anywhere you run Kubernetes.
</p>
</div>
</div>
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7 changes: 4 additions & 3 deletions content/en/docs/started/introduction.md
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## What is Kubeflow

Kubeflow is a community and ecosystem of open-source projects to address each stage in the
machine learning (ML) lifecycle. It makes ML on Kubernetes simple, portable, and scalable.
The goal of Kubeflow is to facilitate the orchestration of Kubernetes ML workloads and to empower
users to deploy best-in-class open-source tools on any Cloud infrastructure.
[machine learning (ML) lifecycle](/docs/started/architecture/#kubeflow-components-in-the-ml-lifecycle)
with support for best-in-class open source
[tools and frameworks](/docs/started/architecture/#kubeflow-ecosystem). Kubeflow makes AI/ML
on Kubernetes simple, portable, and scalable.

Whether you’re a researcher, data scientist, ML engineer, or a team of developers, Kubeflow offers
modular and scalable tools that cater to all aspects of the ML lifecycle: from building ML models to
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