Neil Lawrence appointed Senior Turing AI Fellow
The Alan Turing Institute names Neil among the first five Turing AI Fellows. His five-year Senior Fellowship funds the AutoAI programme on deploying and monitoring machine learning systems.
AutoAI research published at CAIN 2022
17 May 2022
AutoAI research published at CAIN 2022
17 May 2022
A new research paper by Andrei Paleyes, Christian Cabrera and Neil D. Lawrence is published this month at CAIN 2022, the first International Conference on AI Engineering, taking place on 16 and 17 May 2022.
As use of data driven technologies spreads, software engineers are more often faced with the task of solving a business problem using data-driven methods such as machine learning (ML) algorithms. Deployment of ML within large software systems brings new challenges that are not addressed by standard engineering practices and as a result businesses observe high rate of ML deployment project failures. Data Oriented Architecture (DOA) is an emerging approach that can support data scientists and software developers when addressing such challenges. However, there is a lack of clarity about how DOA systems should be implemented in practice.
The paper, presented by Andrei Paleyes later this month, considers Flow-Based Programming (FBP) as a paradigm for creating DOA applications. It empirically evaluates FBP in the context of ML deployment on four applications that represent typical data science projects, comparing it too Service Oriented Architecture (SOA) as a baseline. Results reveal that FBP is a suitable paradigm for data collection and data science tasks, and is able to simplify data collection and discovery when compared with SOA.
For further information, visit: https://conf.researchr.org/track/cain-2022/cain-2022#event-overview
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