Challenges in deploying machine learning : a survey of case studies

In recent years, machine learning has received increased interest both as an academic research field and as a solution for real-world business problems. However, the deployment of machine learning models in production systems can present a number of issues and concerns. This survey reviews published reports of deploying machine learning solutions in a variety of use cases, industries and applications and extracts practical considerations corresponding to stages of the machine learning deployment workflow. Our survey shows that practitioners face challenges at each stage of the deployment. The goal of this paper is to layout a research agenda to explore approaches addressing these challenges

Effectiveness and resource requirements of test, trace and isolate strategies for COVID in the UK

We use an individual-level transmission and contact simulation model to explore the effectiveness and resource requirements of various test-trace-isolate (TTI) strategies for reducing the spread of SARS-CoV-2 in the UK, in the context of different scenarios with varying levels of stringency of non-pharmaceutical interventions. Based on modelling results, we show that selfisolation of symptomatic individuals and quarantine of their household contacts has a substantial impact on the number of new infections generated by each primary case. We further show that adding contact tracing of non-household contacts of confirmed cases to this broader package of interventions reduces the number of new infections otherwise generated by 5–15%. We also explore impact of key factors, such as tracing application adoption and testing delay, on overall effectiveness of TTI

Challenges in Deploying Machine Learning: A Survey of Case Studies

In recent years, machine learning has transitioned from a field of academic research interest to a field capable of solving real-world business problems. However, the deployment of machine learning models in production systems can present a number of issues and concerns. This survey reviews published reports of deploying machine learning solutions in a variety of use cases, industries, and applications and extracts practical considerations corresponding to stages of the machine learning deployment workflow. By mapping found challenges to the steps of the machine learning deployment workflow, we show that practitioners face issues at each stage of the deployment process. The goal of this article is to lay out a research agenda to explore approaches addressing these challenges.Senior AI Fellowship received by Prof. Neil D. Lawrence from UKRI through Alan Turing Institut