Services

Monthly Advisory Services

$3,000 to $14,000 per month

Frustrated by the never ending cycle of machine learning experiments? Get to market faster with less wasted time on unsuccessful approaches. My advisory services give you access to computer vision insights from an experienced scientist/researcher when you need them.

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Distribution Shift Diagnostic

$10,000

Are you confident that your computer vision models will generalize to the variations and batch effects in your inference images? Get a clear strategy for validating your models and improving their generalizability.

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Foundation Model Assessment

$5,000

Is training new models from scratch for every project becoming too costly? Is labeled data scarce or expensive to obtain? Foundation models are popping up everywhere – you might benefit from one trained on your proprietary image dataset. Get a clear perspective on whether you can benefit from a domain-specific foundation model.

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1 Hour Strategy Session

$750

What if you could talk to an expert quickly? Are you facing a specific computer vision or machine learning challenge? Do you have a pressing question? Schedule a 1 Hour Strategy Session now. Ask me anything about whatever challenges you’re facing. I’ll give you no-nonsense advice that you can put into action immediately.

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These services might not be a good fit if you’re only looking for:

How to setup your data infrastructure or annotate your data
Which MLOps tools to use
Model training and validation done for you
Model deployment

Schedule a free call to discuss which service is best for you

Book a Free Discovery Call

Don’t Outsource…
Insource - and Empower Your Team

While machine learning certainly can bring new insights, precision, and efficiency, it takes time to build the technology to do it. And the path to a successful solution is generally not linear.

Many companies outsource machine learning projects. While this can be successful for well-defined applications with a limited scope, high impact projects are different. These projects are often part of a company’s core technology, so they prefer to keep the intellectual property development in house. But they may not have navigated the unique complexities of a machine learning project before. I can help you build the technology and your team in house, ensuring a smoother path to success.

My goal is not just to get you to an ideal solution but to ensure your team also understands how it works so that they can modify it and adapt to new challenges that arise. I will be there to help you navigate these obstacles as needed, but I consider my role most successful if I have transferred this knowledge to your team.

Our team was fortunate to work with Heather on a short-term project, where she was a critical contributor to establishing a framework for future studies.

Keith Wharton Ultivue, VP Medical Director

Working with Heather and Pixel Scientia Labs was a great choice for us because of her extensive experience in cancer detection, imaging, and machine learning. Based on this background and expertise, she was able to support the team as we worked to empower our imaging solutions through AI and machine learning.

Alan Kersey, CytoVeris, President & CEO

Heather’s knowledge of the current state of the art within the digital pathology field is second to none. Discussions of prior art enabled our team to focus on novel research and refine our current AI methodologies for clinical research.

Build an Interdisciplinary Team

I’ve worked with teams spanning many different disciplines and the best results tend to come from a combination of

Pathologists, remote sensing scientists, or other domain experts who understand the data and how it is collected,
Data engineers who gather and organize it,
Machine learning engineers who train and analyze models, and
Software engineers who bring all the pieces together and create a robust system.

Due to the interdisciplinary nature of teams, communication is key.

I can help you in building this team. We start by setting reasonable goals for the project and determine the resources needed. From initial models, I then assess possible next steps including gathering and labeling more data, cleaning data, extending models, and improving computational efficiency. More resources (human or computer) may be needed as the project progresses.