From Data to Decisions - Understanding How AI Models Learn

Explore the foundational process of training machine learning models through this comprehensive 55-minute webinar that focuses specifically on computer vision applications. Learn the essential stages of model development, from initial data preparation through algorithm selection, and discover how each step directly influences performance and accuracy outcomes. Gain practical insights through live demonstrations and conceptual discussions that illuminate how vision models are constructed and validated, with techniques that extend to broader predictive analytics applications. Examine common challenges in model training workflows and discover proven strategies to overcome them, emphasizing the critical importance of simplicity, modular design approaches, and thoughtful evaluation methodologies. Master core concepts including the complete model training lifecycle, understanding why data quality serves as the foundation for success, comprehending inferencing processes and validation metrics, developing modeling intuition beyond traditional engineering mindsets, identifying and avoiding frequent training pitfalls, implementing parsimony and modularity principles in design, and utilizing checkpoints effectively to monitor and refine progress throughout development. Presented by industry experts Cal Foshee and Eric Gamble from IBM, along with Erik Smith from Dell Technologies, this session provides actionable knowledge for transitioning from data collection to informed decision-making through AI model development.