---
title: Experimentation capabilities (video)
description: Experience the breadth of experimentation, speed of experimentation, and flexibility that are key strengths of the DataRobot AI Platform.
---

# Experimentation capabilities (video) {: #experimentation-capabilities-video }

Watch a rapid tour of a variety of use cases that demonstrate:

* Five major data types&mdash;numerics, categorical, text, geospatial, and images.
* Nine major problem types&mdash;classification, regression, clustering, multilabel, anomaly detection, forecasting, time series clustering, time series anomaly detection, and generative AI.
* More than 40 modeling techniques that are specific to each problem type.

Each quick experiment demo was built with DataRobot's automation and results in a fully deployable machine learning pipeline.

<hr>

## Summary of support {: #summary-of-support }

This video discusses the AI Experimentation capabilities in DataRobot. Specifically,  what data types can be used, what problem types can be solved, what modeling techniques can be applied, and what external tools and technologies can be incorporated into your solution design.

<div style="position:relative;padding-bottom:56.25%;">
 <iframe style="width:100%;height:100%;position:absolute;left:0px;top:0px;" title="Ingest AWS S3 Data into DataRobot" frameborder="0" width="100%" height="100%"
 allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" src="https://www.youtube.com/embed/o7Z2gHuBplY?si=lDvOzCJK0Dom1Mg1" allowfullscreen></iframe>
</div>
<br>

<hr>

## Read more {: #read-more }

* [Workbench in 5](gs-dr5/index){ target=_blank }
* [Fundamentals of DataRobot (Classic)](gs-dr-fundamentals){ target=_blank }
* [Basic model workflow (Classic)](model-data){ target=_blank }
