While working with a variety of organizations on computer vision projects, I’ve witnessed the long and iterative development cycles. Every dataset is different, so it does take some experimentation to get a robust model.

But some of this experimentation could be reduced by following three key principles:

• Identifying sources of variation or other data challenges – domain experts are essential for this
• Understanding prior work – a literature review can find candidate solutions and unresolved challenges
• Validating early and often to reveal failure modes – essential for making improvements

Model development progresses much faster and smoother when you understand the challenges in your dataset and can try solutions that have worked for others on similar data. And proper validation can focus your efforts to improve your models and help you recognize major obstacles earlier.

I guide organizations in following these principles. Given the unique characteristics of a dataset, I help them identify the best path to robust and generalizable models, enabling them to take their products to market sooner.