serves as a basis for future research and addresses open questions. Our paper provides an overview of the current state of the research on the adoption of AI in a production-specific context, which forms a basis for further studies as well as a starting point for a better understanding of t...
AI at the edge enables more data for predictive analytics and model-driven decision support, pairing in-camera video analytics and deep learning capabilities. AI at the Edge Changing Safety and Security Learn about AI at the edge partners and solutions for safety and security, including Intel®...
Details of the digestive system in the midgut of Coptotermes formosanus Shiraki Wood-feeding termites have evolved an efficient cellulose-decomposing system. The termite has two independent cellulose-digesting systems: one in the midgu... F Ai,M Hojo,T Aoyagi,... - 《Journal of Wood Science》...
It is quite common that modern production devices include good functions for data acquisition, but they are either not used at all or the data collected by the system are transferred to data bases without exploiting them thoroughly. In this respect, CI which utilizes process history would offer ...
How can we evaluate the quality of a model’s predictions in production? How can we test the entire AI-enabled system, not just the model? What lessons can we learn from software testing, automated test case generation, simulation, and continuous integration for testing for production machine ...
The simplest way to serve AI/ML models in productionWhy Truss?Write once, run anywhere: Package and test model code, weights, and dependencies with a model server that behaves the same in development and production. Fast developer loop: Implement your model with fast feedback from a live ...
to develop an AI model predicting machinery faults and scheduling predictive maintenance during scheduled down times. We help with end to end implementation of the solution including: Identification where the technology could be used in your operations, IoT sensors implementation, Big Data plaform set...
In production ML workflows, data scientists and engineers frequently try to improve performance using various methods, such asAutomatic model tuning with SageMaker AI, training on additional or more-recent data, improving feature selection, using better updated instances and serving containers. You can ...
“This new industrial edge computer, an integration of Intel® AI technology and Hitachi’s highly reliable control system design and manufacturing capability, incorporates an image recognition platform using AI and deep learning on the manufacturing floor. Hitachi is accelerating field digitalization, ...
Fig. 2. The DT is usually considered an structural, optimization or simulation model of the physical system. The twinning cycle comprises a physical-to-virtual link which analyzes physical systems measurements and changes the digital twin, and virtual-to-physical link which enables control of the ...