Building AI has never been easier. With foundation models, open-source tools, and modern frameworks, teams can create working prototypes in days. But many systems that look strong in development begin to fail when exposed to real users, real environments, and real variability. In our experience, the issue is often not the model—it is the dataset. Poor coverage, inconsistent labels, unrealistic augmentation, and misleading evaluation splits can quietly limit performance long before deployment.
On Tuesday, May 12 @ 12pm ET, join SentiMetrix Founder and President Vadim Kagan for a practical 30 minute webinar based on lessons learned during NIH-funded PathML development.
PathML is a real-world AI system built to analyze human movement from video, and the project forced us to solve the kinds of data problems many startups encounter: plateaued performance, hidden labeling noise, lab-to-production accuracy drops, and expensive data collection decisions. We’ll share the approaches that helped us fix them, and discuss the lessons that transfer to other domains including computer vision, sensor analytics, medical AI, and behavior-based systems.
You’ll learn how to determine when you have enough data (and when more data is just more cost), how to design labels your models can learn from consistently, when augmentation helps versus harms, and how to structure train/test splits that produce metrics you can actually trust.
What You'll Learn:
How to determine the right dataset size for your specific task — and why collecting more data is not always the right move
How to design labels that produce consistent annotations
When and how to use augmentation techniques to extend your dataset without degrading model performance
How to design train/validation/test splits that prevent leakage and produce evaluation metrics you can actually trust
Who Should Attend?
Teams building AI-powered products and features
Teams working with real-world data, including video, text, sensors, and clinical records
Teams moving from prototype to production
Teams whose models perform well in the lab but struggle in the field
Technical founders managing lean budgets ahead of a funding or pilot milestone
About the Speaker. Vadim Kagan, SentiMetrix Founder and President, has over 30 years of experience in software and information systems. He has served as Principal Investigator, Co-PI, and Program Manager on DARPA- and U.S. Army MEDCOM–sponsored programs. His work spans behavioral analytics and PTSD-related signal detection, and he has managed the transition of machine learning technologies into deployable operational solutions.