In 2015 launch YOLO — a high-performance computer vision model that could make predictions for real-time object detection — began an avalanche of progress that accelerated the transition of computer vision from research to the market.
Since then it has been an exciting time for startups as entrepreneurs continue to open Computer vision use cases are in everything from retail and agriculture to construction. With lower computational costs, greater model accuracy, and rapid proliferation of raw data, more startups are turning to computer vision to find solutions to problems.
However, before founders start building AI systems, they must carefully consider their risk appetite, data management practices, and strategies for securing the future of their AI stack.
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Below are four factors that founders should consider when deciding whether to build computer vision models.
Is deep learning the right solution for my problem?
It may sound crazy, but the first question founders should ask themselves is whether they even need to use a deep learning approach to solve their problem.
During my time in finance, I often saw that we were hiring a new employee straight from the university who would like to use the latest deep learning model to solve a problem. After spending time working on the model, they concluded that using the linear regression variant worked better.
To avoid the so-called gap between prototype and production, founders must carefully consider the performance characteristics needed to deploy a model.
Moral of this story?
Deep learning may seem like a futuristic solution, but in fact, these systems are sensitive to many small factors. Often you can already use an existing and simpler solution, such as a “classic” algorithm, that gives the same good or better result at less cost.
Consider the problem and solution from all angles before building a deep learning model.
Deep learning in general and computer vision in particular hold great promise for creating new approaches to solving old problems. However, building these systems comes with an investment risk: you’ll need machine learning engineers, lots of data, and validation mechanisms to put these models into production and build a working AI system.
It’s best to evaluate if a simpler solution can solve your problem before embarking on such a massive effort.
Conduct a thorough risk assessment
Before creating any AI system, founders must consider their risk appetite, which means assessing the risks that arise both at the application level and at the research and development stage.
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