Computer Vision for People & Planetary Health

Reducing the trial-and-error of machine learning for startups

Frustrated by the never ending cycle of machine learning experiments?

Models that perform well on your training data but fail on images from a different source?
Struggling to keep up with the rapidly advancing field?
Unsure whether you’re focusing on the best model types for your application?
Data challenges like multispectral images, noisy labels, or small training sets?

Get to market faster with less wasted time on unsuccessful approaches

Support your team in building robust and generalizable models
Stay up-to-date with the latest and greatest tools and techniques
Follow best practices for your unique challenges
Develop unbiased models that provide the most valuable insights

Meet Heather D. Couture, PhD
Consultant & Researcher

While working with a variety of startups 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.