Sight Machine Inc. announced it is collaborating with NVIDIA Corp. to apply machine learning to turn the chaos of 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’s manufacturing data foundation solves the central challenge in the digital transformation of manufacturing, turning raw plant data into a data foundation that captures manufacturing in digital form. Sight Machine then applies data analytics and AI/ML in real time to solve the core problems in manufacturing, which include improving performance measures like throughput, quality and downtime, and sustainability measures including energy use and scrap.
However, a bottleneck – common to all complex data environments but most difficult in manufacturing – remains near the front end of the process: data labeling, sometimes referred to as tag mapping. Before a data stream can be incorporated into a data model or data foundation, one must know what that particular type of data represents and where it came from. To apply AI at scale, enterprise manufacturers must understand the data they analyze.
A modern factory may generate streams of data from 100,000 or more point sources, such as individual sensors, and large enterprises must manage millions. Many companies have been collecting industrial data for years in a data lake or historian, assuming that once they get data aggregated they will be able to derive value from it. Few companies have managed to identify all those data points in a way that makes them useful.
Further complicating the picture: although historians and data lakes improve data accessibility, they often lose associated metadata from the PLC or other source devices. The result is a large number of data values that have been removed from their context, making it even more difficult to understand if or why the data is useful.
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 organize 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,” said Jon Sobel, CEO and Co-Founder of Sight Machine. “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.”
“NVIDIA GPU acceleration of manufacturing will allow industrial companies to unlock trillions of dollars in value,” said Piyush Modi, global development leader and chief strategist for the industrial sector at NVIDIA. “Sight Machine’s adoption of the NVIDIA AI platform will help customers improve their understanding of data and ultimately speed up the digital transformation on their factory floors.”