New survey on adoption of data-oriented architecture
A new survey from the ML@CL group explores adoption of Data-Oriented Architecture paradigm for ML deployment.
Overview
While excitement about the potential of artificial intelligence (AI) technologies continues to build, a gap is emerging between our aspirations for the benefits of AI and our ability to deploy these technologies to tackle real-world challenges.
The AutoAI Programme scales our ability to deploy safe and reliable AI solutions, driving innovation in machine learning-enabled techniques for deploying, maintaining, and understanding AI systems. By investigating how to decompose AI systems into their component parts, how to manage data in system development, and how to monitor performance in deployment, AutoAI will develop a new AI design and engineering paradigm.
These innovations require research advances to develop more sophisticated machine learning models and integrate them in wider systems, new types of infrastructure and programming techniques to solve technical challenges at scale, a shift towards data-oriented technical infrastructures and organisational practices, and wider organisational, legal and policy interventions to influence AI adoption.
AutoAI will address the entire pipeline of AI system development, from data acquisition to decision making. It will develop policies that enable trustworthy access to data, build effective research collaborations that embed stakeholder needs in research design, and promote an open culture of AI and software engineering.
Related People
Andrei Paleyes
Visiting Researcher, Cambridge University
Christian Cabrera Jojoa
Assistant Research Professor, Cambridge University
Jess Montgomery
Director, ai@cam and Accelerate Science, Cambridge University
Morine Amutorine
Africa Sandboxes Forum Lead, Datasphere Initiative
Neil D. Lawrence
The DeepMind Professor of Machine Learning, Cambridge University
Diana Robinson
Visiting Researcher, Cambridge University
Project Alumni
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Recent Publications
Machine Learning Systems: A Survey from a Data-Oriented Perspective
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Requirements are All You Need: The Final Frontier for End-User Software Engineering
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Increasing data sharing and use for social good: Lessons from Africa’s data-sharing practices during the COVID-19 response
Data & Policy, 6:
The Systems Engineering Approach in Times of Large Language Models
58th Hawaii International Conference on System Sciences (HICSS-58), :
Self-sustaining software systems (S4): Towards improved interpretability and adaptation
Proceedings of the 1st International Workshop on New Trends in Software Engineering, :