Add Hyper-Datasets intro (#766)

docs/hyper_datasets.md

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+---
+title: Hyper-Datasets
+---
+
+:::important Enterprise Feature
+Hyper-Datasets are available under the ClearML Enterprise plan
+:::
+
+<div class="vid">
+<iframe style={{position: 'absolute', top: '0', left: '0', bottom: '0', right: '0', width: '100%', height: '100%'}} 
+        src="https://www.youtube.com/embed/1VliYRexeLU?si=WAXIdAwsja7D0lxH" 
+        title="YouTube video player" 
+        frameborder="0" 
+        allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen" 
+        allowfullscreen>
+</iframe>
+</div>
+
+<br/>
+
+ClearML's **Hyper-Datasets** are an MLOps-oriented abstraction of your data, which facilitates traceable, reproducible model development
+through parameterized data access and metadata version control. 
+
+Hyper-Datasets is a data management system specifically tailored for handling unstructured data, like text, audio, or 
+visual data. You can create, manage, and version your datasets. Datasets can be set up to inherit from other datasets, so
+data lineages can be created, and users can track when and how their data changes. In the ClearML Enterprise's WebApp, 
+you can view a dataset's version history, as well as its contents, including annotations, metadata, masks, and other 
+information.
+
+![Frame viewer](img/hyperdatasets/web-app/dataset_example_frame_editor.png)
+
+The basic premise of Hyper-Datasets is that a user-formed query is a full representation of the dataset used by the ML/DL 
+process. Hyper-Datasets decouple metadata from raw data files, allowing you to manipulate metadata through sophisticated
+queries and parameters that can be tracked through the experiment manager. You can clone experiments using different 
+data manipulations--or **DataViews**--without changing any of the hard coded values, making these manipulations part of 
+the experiment. 
+
+ClearML **Enterprise**'s Hyper-Datasets supports rapid prototyping, creating new opportunities such as: 
+* Hyperparameter optimization of the data itself
+* QA/QC pipelining
+* CD/CT (continuous training) during deployment
+* Enabling complex applications like collaborative (federated) learning. 
+
+
+For more information, see [Hyper-Datasets](hyperdatasets/overview.md).
+