Starting at $30,000
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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.
I’ve worked with teams spanning many different disciplines and the best results tend to come from a combination of
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.