Sight Machine is collaborating with Nvidia to apply machine learning to turn factory data into insights for improving production. The collaboration connects Sight Machine’s manufacturing data foundation with Nvidia’s AI platform to break through the last bottleneck in the digital transformation of manufacturing, preparing raw factory data for analysis.
Sight Machine’s manufacturing intelligence will guide Nvidia machine learning software running on Nvidia GPU hardware to process two or more orders of magnitude more data at the start of digital transformation projects.
Sight Machine is aiming to break the data labeling bottleneck by linking its streaming data pipeline with the Nvidia AI platform, running on Microsoft Azure infrastructure, to map data to assets at a global scale. The collaboration with Nvidia will enable Sight Machine to organise orders of magnitude more data than is currently feasible, at high speeds and without consuming excessive time from data scientists, controls engineers, or other subject matter experts.
“This work addresses the last critical bottleneck in manufacturing transformation and will rapidly accelerate day-to-day use of AI in plants. We’re taking our data discovery/introspection/analysis loop, with heuristics developed over a decade of mapping data, and turbocharging it with Nvidia’s AI platform. This approach will prepare factory data for analysis at a previously unimaginable speed, bringing what currently takes two to three months of effort down to hours and days. This is all possible because of the decade of experience being brought to the problem through AI,” said Jon Sobel, CEO, and Co-Founder of Sight Machine.
Accelerating data labelling will enable Sight Machine to quickly onboard large enterprises with massive data lakes. It will automate and accelerate work and lead to even faster time to value. While similar automated data mapping technology is being developed for specific data sources or well documented systems, Sight Machine is the first to use data introspection to automatically map tags to models for a wide variety of plant floor systems.
