MLOps in the Metaverse: Human-Centric Continuous Integration

The metaverse is a virtual world that exists entirely in a computer-generated environment, and it offers a new frontier for machine learning. One of the major challenges for using machine learning in the metaverse is MLOps (Machine Learning Operations), an emerging field that focuses on deploying and managing machine learning models in production. It has been widely acknowledged that machine learning models require a large amount of data to learn and make accurate predictions, and such data is generated progressively in real-time as human users interact with the metaverse. Due to the human-centric nature of the metaverse, it goes without saying that, once deployed, models need to be able to adapt to the constantly changing interactive environment and still make accurate predictions. Borrowing a page from software engineering, in this paper, we explore the design space of human-centric continuous integration in metaverse environments, where labeled data samples accumulated with explicit human interactive behavior (e.g., using virtual reality or augmented reality headsets) are used for fine-tuning a deployed deep learning model over a sustained period of time. We propose SPIN, a new mechanism that efficiently utilizes data samples collected from a large number of participating human users over time to fine-tune a deployed model that is shared across all the users. In an extensive array of experimental results using image classification and state-of-the-art
YOLOv8
object detection models as case studies, we show that SPIN outperforms FedBuff, a state-of-the-art asynchronous FL mechanism from conventional federated learning, by a substantial margin.