Apurva Godghase
Senior Computer Vision Engineer, Brambles
Apurva is a Senior Computer Vision Engineer at Brambles, with over seven years of R&D experience across diverse industrial domains. At Brambles, she specializes in designing and deploying cutting-edge machine learning and computer vision IoT prototypes to enhance supply chain efficiencies. Previously, at Spectrum, she contributed to Industrial IoT R&D, focusing on machine learning for wireless access, video analytics, and predictive air quality modeling using edge device data. Her tenure at Lands’ End involved expertise in sales forecasting, pricing strategies, and product similarity analysis.
She has also conducted independent research on Large Language Models (LLMs), exploring their applications in supply chain solutions. She is also a member of the Harvard Business Review Advisory Council, a research community of business professionals.
All Sessions by Apurva Godghase
Track B: Strategies for Image Dataset Curation from High-Volume Industrial IoT data
Breakout RoomIn Industrial IoT for Supply chain, and logistics, massive amounts of data is generated by edge devices that capture data continuously. For embedded vision systems, managing the sheer volume of images and metadata can be challenging. Selecting a diverse subset of high-quality data is crucial for effective modeling and analysis. This work outlines a comprehensive method for selecting relevant images from an extensive dataset to build a high-quality image database for building and monitoring computer vision and machine learning models. This systematic approach not only enhances the efficiency of data management in industrial IoT applications but also improves the generalizability and accuracy of Computer Vision learning models.