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Found 15 papers, 10 papers with code
Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation
S4 builds knowledge loops between all available knowledge sources that define modern software systems to improve their interpretability and adaptability.
Automated discovery of trade-off between utility, privacy and fairness in machine learning models
Thus the trade-off between fairness, privacy and performance of ML models emerges, and practitioners need a way of quantifying this trade-off to enable deployment decisions.
Causal fault localisation in dataflow systems
Dataflow computing was shown to bring significant benefits to multiple niches of systems engineering and has the potential to become a general-purpose paradigm of choice for data-driven application development.
Dataflow graphs as complete causal graphs
Component-based development is one of the core principles behind modern software engineering practices.
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow.
Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective
The survey shows the design decisions of the systems and the requirements these satisfy.
Desiderata for next generation of ML model serving
Inference is a significant part of ML software infrastructure.
A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger design
We present HIghly Parallelisable Pareto Optimisation (HIPPO) -- a batch acquisition function that enables multi-objective Bayesian optimisation methods to efficiently exploit parallel processing resources.
An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment Context
Data Oriented Architecture (DOA) is an emerging approach that can support data scientists and software developers when addressing such challenges.
Emulation of physical processes with Emukit
Decision making in uncertain scenarios is an ubiquitous challenge in real world systems.
Towards better data discovery and collection with flow-based programming
Our main conclusion is that FBP shows great potential for providing data-centric infrastructural benefits for deployment of ML.
Good practices for Bayesian Optimization of high dimensional structured spaces
The increasing availability of structured but high dimensional data has opened new opportunities for optimization.
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.
Causal Bayesian Optimization
This paper studies the problem of globally optimizing a variable of interest that is part of a causal model in which a sequence of interventions can be performed.
Automatic Discovery of Privacy-Utility Pareto Fronts
Differential privacy is a mathematical framework for privacy-preserving data analysis.
