https://dasya.itu.dk/tags/ml-model-updates/ Recent content in ML model updates on DASYA Hugo -- gohugo.io en-us © DASYA, IT University of Copenhagen Mon, 13 May 2024 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-tiny-modelupdates/ Mon, 13 May 2024 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-tiny-modelupdates/ One of the key challenges with enabling efficient machine learning on resource-constrained devices is keeping the machine learning models deployed on these devices up-to-date without frequent retraining. This requires exploring the impact of different model update mechanisms at the edge. This project would be suitable as a standalone project or BSc or MSc thesis at ITU during Fall 2024. If you are interested in resource-constrained hardware, benchmarking, and machine learning in general, this project would be a great fit for you. https://dasya.itu.dk/for-students/proposals/archive/pito-tiny-modelsizes/ Mon, 13 May 2024 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-tiny-modelsizes/ To enable efficient data processing and machine learning on resource-constrained devices has many challenges. One is fitting the models into the restrictive memory and compute resources of these devices. In this project, first, we would like to explore the landscape of foundational, generative-AI, language, etc. models with respect to their size and compute needs to understand what could be a fit to be deployed at the edge. Then, we would like to characterize the performance of some edge model deployments on a chosen edge device and building on the analysis in the first step.