From Data to Decisions: Understanding How AI Models Learn

Join us for an insightful webinar and demonstration as we explore the foundational process of training machine learning models, with a special focus on computer vision applications. This session will guide participants through the essential stages of model development—from data preparation to algorithm selection—highlighting how these steps influence performance and accuracy. Through practical demonstrations and conceptual discussions, attendees will gain a deeper understanding of how vision models are built and validated, and how these techniques can be extended to broader predictive analytics. We’ll also examine common challenges and share strategies to overcome them, emphasizing the value of simplicity, modular design, and thoughtful evaluation. Key Takeaways: • Core steps in the model training lifecycle • Why data quality is critical to success • Understanding inferencing and metrics for model validation • Shifting from engineering mindset to modeling intuition • Avoiding common pitfalls in training workflows • Embracing parsimony and modularity in design • Using checkpoints to monitor and refine progress