If someone asked you what professional problem you’re addressing at the current time, you’d be able to offer a short overview of the project and why you’ve taken it on. That explanation, and the reasons behind it, are sure to be sound and strong. However, it’s more than likely that it’s not the largest problem at hand for your company. Ask yourself instead a different question: Assuming anything was possible technologically, what problems would you be working on today? Chances are, that’s what you should be worrying about.
Machine-learning technology turns that exploratory intellectual exercise into an actionable reality. After hearing from some Shutterstock customers that it took them longer than they’d like to locate the images they needed, some of our engineers formed a computer-vision team a year ago to address the pitfalls that come with typing words into a search bar. During that time, we developed technology that came to understand the 80 million images inside our collection. We launched it publicly in March.
Machine learning and data science aren’t topics reserved for engineers and technologists, though. To ensure you’re spending your time and resources wisely, you must keep the customer in mind throughout the process. Otherwise, you’ll risk winding up with a fun novelty that doesn’t appeal to anyone other than those who built it. Here are some lessons we discovered from our deep learning: