Mercury Marine · company speakers →
AI and machine learning (ML) are often portrayed as silver bullets for manufacturing challenges, but the real story begins with the data behind these tools. This session explores the ongoing journey of a manufacturing operation attempting to predict utility usage. The project began as a simple data analysis request but quickly expanded into a deeper exploration of data integrity, system complexity, and the integration of data sources. Custom scripting played a critical role in cleaning, analyzing, joining, and modeling the data, uncovering what worked, what failed, and the unexpected noise encountered along the way. Like all data journeys, this one continues today. The experience so far demonstrates how data collection practices, quality, and alignment with AI/ML requirements establish the foundation for better questions and stronger results in future projects.
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