This demo showcases the end-to-end capabilities in the DataRobot Enterprise AI Platform. Using the Utah house price listings dataset, we demo how to predict property prices using a combination of diverse feature types, including numeric, categorical, raw text, images, and geospatial data.
During this demo, we go from raw data to value. To start, we use DataRobot Paxata to prepare the data. Then we use DataRobot Automated Machine Learning with Visual and Location AI to select, train, and test a variety of models to find the most accurate. Using DataRobot MLOps, we deploy the model and easily monitor it in production; once it starts to degrade, we simply hot-swap it for a new challenger model.
Finally, we demonstrate how DataRobot's Use Case Value Tracker enables you to track the value of your predictions so that you can understand the full ROI of your AI initiative. Throughout the demo, you learn how DataRobot's platform provides governance, explainability, and trusted AI for every step of the journey.
If you have any questions or comments about what we've shown here, click Comment and let us know!
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