Jan Metzner

Commissioning industrial robots typically takes weeks of physical testing. Digital twins with real-time physics simulation and multi-agent AI optimization can compress that timeline from weeks to hours.

This hands-on builders’ session guides you through the process of connecting Siemens PLCs at scale, determining the exposed data, and generating an AWS IoT SiteWise hierarchy using generative AI with Amazon Bedrock.

Factories are often full of machines, and it can be cumbersome to connect and map these disparate datasets to a standard machine informational model for future analysis.

In this chalk talk, dive deep into the architecture patterns for factory deployments of the future and hear from Northvolt on how they architected their smart factory. The patterns covered show how you can build predictive quality, asset condition monitoring, machine performance, and predictive maintenance solutions.

Join this workshop to get hands-on experience applying artificial intelligence and machine learning (ML) for industrial use cases. Using a sample product quality image dataset, build and train an ML model with Amazon Lookout for Vision.

Bring your laptop and basic console knowledge to this hands-on workshop to get direct experience applying AI and ML to industrial operations use cases such as predictive quality and rotating equipment condition monitoring. Learn how to use a sample product quality image dataset to build and train an ML model with Amazon Lookout for Vision to detect quality issues on a production line.

Traditional manufacturing execution systems (MESs) are historically based on rigid, legacy technology-based systems or even more challenging on-paper-based inputs. Manufacturers often perceive MES digital transformation as slow, complex, and expensive.

Industrials robots are the heart of the automated factory, but data is trapped in those robots, and customers want to access the data. In this talk, we will show you how to connect industrial robots to the cloud with various protocols.