Data comes in all shapes and sizes. Just like our daily decision-making processes, the more points of view we have, the better the potential outcome. For example, when considering a hospital readmission dataset, we can use traditional features, such as patients’ gender, readmission date, and the number of previous lab procedures. In order to strengthen the artificial intelligence (AI) models and get even better predictions, we can add, for example, an image of a patient’s MRI and raw text from doctors’ notes.
In the webinar DataRobot Visual AI: See the Big Picture with Automated Computer Vision, we explored these issues, discussing how deep learning and computer vision algorithms work and the way companies can apply DataRobot Visual AI for their daily operations.
Visual AI Demystifies Deep Learning
You don’t need to be an expert in data science and deep learning to understand the process of training neural networks and creating models by yourself. DataRobot Visual AI is extremely easy to use. Just drag and drop a zip file containing your images, their labels, and any other set of features you like into DataRobot to get started. You then pick your target feature and hit the Start button in the usual way.
The deep learning models DataRobot builds are just as easy to understand. Visual AI provides detailed blueprints to explain every pre-processing step and which algorithms were used and a variety of human-friendly visual insights to help you interpret results, including image activation maps and row-by-row prediction explanations.
Visual AI is part of DataRobot’s Automated Machine Learning product which is tightly integrated with DataRobot MLOps. MLOps makes it incredibly easy to deploy your deep learning model to your production environment of choice in just a few clicks. You are then able to monitor and manage your Visual AI models over its full production lifecycle in a tightly governed environment.
Example Visual AI Use Cases
How do companies use automated computer vision? In the webinar, we discuss some examples, including the early detection of forest fires from aerial photography or the assessment of hurricane property damage using images from drones in areas that inspectors on the ground cannot access. Another application discussed was the automated capability to distinguish people who are wearing masks from those who are not before being admitted into a building or office.
In the healthcare industry, providers can use more complex models with a wide variety of features to recognize types of skin lesion and the probability of skin cancer developing.
Conclusion
The different ways in which DataRobot Visual AI can be used is limited only by the human imagination. If you are interested in learning more about how Visual AI can add value to your business or organization, be sure to watch the whole webinar, DataRobot Visual AI: See the Big Picture with Automated Computer Vision.
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