- Build 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
Are you frustrated by the never ending cycle of machine learning experiments?
- Do your models perform well on your training data but fail on images from a different source?
- Does your team struggle to keep up with the rapidly advancing field?
- Are you unsure whether you’re focusing on the best model types for your application?
- Do you have data challenges like multispectral images, noisy labels, or small training sets?
Get results faster with less wasted time on unsuccessful approaches
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 help organizations follow 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.