Building AI has never been easier. With foundation models and modern frameworks, teams can develop a working prototype in days. But as experienced innovators know all too well, something changes when those systems meet the real world. The model that worked in the demo suddenly begins to behave unpredictably, and performance numbers that looked impressive become unreliable.
In most cases, the problem is not the model. It is the dataset.
Modern systems - including those built on foundational models - require clean data as a representation for the real world. But collecting and labeling that data can quickly become expensive. This challenge is made worse by overcollection costs caused by teams unsure of how much data they actually need or how to effectively structure it for training.
The good news is that building a balanced, production-ready dataset does not require unlimited data collection. With the right strategy, teams can reduce unnecessary data acquisition, extend existing datasets through augmentation, and design verification workflows that maintain quality without excessive cost.
On Wednesday, April 22nd at 12pm ET, you're invited to join Vadim Kagan, Founder and President of SentiMetrix, for a 20-minute session (followed by Q&A) on how to build balanced, production-ready datasets without overspending to ensure your AI project launches with the accuracy the market demands.
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.