4 questions to ask before building a computer vision model

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In 2015, the launch of YOLO — a high-performing computer vision model that could produce predictions for real-time object detection — started an avalanche of progress that sped up computer vision’s jump from research to market.

It’s since been an exciting time for startups as entrepreneurs continue to discover use cases for computer vision in everything from retail and agriculture to construction. With lower computing costs, greater model accuracy and rapid proliferation of raw data, an increasing number of startups are turning to computer vision to find solutions to problems.

However, before founders begin building AI systems, they should think carefully about their risk appetite, data management practices and strategies for future-proofing their AI stack.


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Below are four factors that founders should consider when deciding to build computer vision models.

Is deep learning the right tool for solving my problem?

It may sound crazy, but the first question founders should ask themselves is if they even need to use a deep learning approach to solve their problem.

During my time in finance, I often saw that we’d hire a new employee right out of university who would want to use the latest deep learning model to solve a problem. After spending time working on the model, they’d come to the conclusion that using a variant of linear regression worked better.

To avoid falling into the so-called prototype-production gap, founders must think carefully about the performance characteristics required for model deployment.

The moral of the story?

Deep learning might sound like a futuristic solution, but in reality, these systems are sensitive to many small factors. Often, you can already use an existing and simpler solution — such as a “classical” algorithm — that produces an equally good or better outcome for lower cost.

Consider the problem, and the solution, from all angles before building a deep learning model.

Deep learning in general, and computer vision in particular, hold a great deal of promise for creating new approaches to solving old problems. However, building these systems comes with an investment risk: You’ll need machine learning engineers, a lot of data and validation mechanisms to put these models into production and build a functioning AI system.

It’s best to evaluate whether a simpler solution could solve your problem before beginning such a large-scale effort.

Perform a thorough risk assessment

Before building any AI system, founders must consider their risk appetite, which means evaluating the risks that occur at both the application layer and the research and development stage.

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