A case for managed and model-less inference serving

A case for managed and model-less inference serving Yadwadkar et al., HotOS'19 HotOS’19 is presenting me with something of a problem as there are so many interesting looking papers in the proceedings this year it’s going to be hard to cover them all! As a transition from the SysML papers we’ve been looking at recently, … Continue reading A case for managed and model-less inference serving

Beyond data and model parallelism for deep neural networks

Beyond data and model parallelism for deep neural networks Jia et al., SysML'2019 I’m guessing the authors of this paper were spared some of the XML excesses of the late nineties and early noughties, since they have no qualms putting SOAP at the core of their work! To me that means the "simple" object access … Continue reading Beyond data and model parallelism for deep neural networks

PyTorch-BigGraph: a large-scale graph embedding system

PyTorch-BigGraph: a large-scale graph embedding system Lerer et al., SysML'19 We looked at graph neural networks earlier this year, which operate directly over a graph structure. Via graph autoencoders or other means, another approach is to learn embeddings for the nodes in the graph, and then use these embeddings as inputs into a (regular) neural … Continue reading PyTorch-BigGraph: a large-scale graph embedding system

Towards federated learning at scale: system design

Towards federated learning at scale: system design Bonawitz et al., SysML 2019 This is a high level paper describing Google’s production system for federated learning. One of the most interesting things to me here is simply to know that Google are working on this, have a first version in production working with tens of millions … Continue reading Towards federated learning at scale: system design

Continuous integration of machine learning models with ease.ml/ci

Continuous integration of machine learning models with ease.ml/ci: towards a rigorous yet practical treatment Renggli et al., SysML'19 Developing machine learning models is no different from developing traditional software, in the sense that it is also a full life cycle involving design, implementation, tuning, testing, and deployment. As machine learning models are used in more … Continue reading Continuous integration of machine learning models with ease.ml/ci