Declarative Data Serving: The Future of Machine Learning Inference on the Edge

Recent advances in computer architecture and networking have ushered in a new age of edge computing, where computation is placed close to the point of data collection to facilitate low-latency decision making. As the complexity of such deployments grow into networks of interconnected edge devices, getting the necessary data to be in “the right place at the right time” can become a challenge. We envision a future of edge analytics where data flows between edge nodes are declaratively configured through high-level constraints. Using machine learning model-serving as a prototypical task, we illustrate how the heterogeneity and specialization of edge devices can lead to complex, task-specific communication patterns even in relatively simple situations. Without a declarative framework, managing this complexity will be challenging for developers and will lead to brittle systems. We conclude with a research vision for database community that brings our perspective to the emergent area of edge computing. PVLDB Reference Format: Ted Shaowang, Nilesh Jain, Dennis D. Matthews, and Sanjay Krishnan. Declarative Data Serving: The Future of Machine Learning Inference on the Edge. PVLDB, 14(11): 2555 2562, 2021. doi:10.14778/3476249.3476302