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.
Machine learning systems for real-world challenges
About
ML@CL is creating safe and reliable machine learning systems that can be deployed to tackle real-world challenges.
Amidst growing excitement about AI, a gap has emerged between our aspirations for AI and our ability to deploy these technologies to tackle real-world challenges. ML@CL aims to bridge this gap through innovations in modelling, systems and software engineering for machine learning deployment; the application of AI for scientific discovery; and the development of policy frameworks for trustworthy and beneficial AI.
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Research
Our work addresses the full pipeline of AI system development, from data acquisition, through model development to system deployment.
We work closely with practitioners, domain experts and policymakers to understand and respond to the real-world challenges associated with AI deployment. Our research spans the fundamentals of machine learning methods, statistical emulation and uncertainty quantification, real-time inference and decision-making, systems design, the application of AI in science and industry, data stewardship, and AI policymaking. Find out more about current projects on our Research pages.
Our Research
27 February 2023
5 July 2022
New grants programme to help Cambridge researchers deploy AI for science
(5 July 2022) The Accelerate Programme in collaboration with C2D3 is offering small grants for Cambridge University researchers pursuing innovative applications of AI, in research or real-world contexts. Funding can support a variety of activities, including events, workshops, teaching, software development, or research, with a focus on interdisciplinary collaboration.
7 June 2022
New Python for Science course will help researchers learn programming skills
The Accelerate Science Programme and education technology company Cambridge Spark are offering researchers across Cambridge University a new self-learning module so they can learn programming in Python.