Is ML the solution for making sense of vast collections of images? In demo form, it looks amazing. But does it really provide actionable information for you, or does it junk up your tags with a lot of low value (and wrong) information? Time for a taste test! In this presentation, you’ll see the results of real world testing from leading services - Google, Amazon, Microsoft and Clarifai. Our test set includes a wide variety of images representing multiple industries and tagging challenges. We’ll show you where each ML shines, and where each misfires, and how the serviuces have evolved. Armed with our evidence and conclusions, you can decide if it’s delicious, or not yet ready to eat. As a bonus, we’ll show you how to easily run your own test on tens of thousands of images for under $200.
Other Resources / Information
- Get a solid idea of the info that Machine Learning can currently add to image collections. Understand what it's good and bad for.
- Get a handle on the differences between ML services and how each can help you. Get a better idea of how to evaluate your options.
- There is no substitute for some real-world testing on your own material - at scale - if you want to determine the value of a service. Here's how.
Peter Krogh, Author, DAM Useful Publishing
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