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Challenges in Deploying and Monitoring Machine Learning Systems
Alessandra Tosi · Andrei Paleyes · Christian Cabrera · Fariba Yousefi · S Roberts

Fri Dec 09 01:00 AM -- 11:15 AM (PST) @ Virtual
Event URL: https://sites.google.com/view/dmmlsys-neurips2022/home »

The goal of this event is to bring together people from different communities with the common interest in the Deployment of Machine Learning Systems.

With the dramatic rise of companies dedicated to providing Machine Learning software-as-a-service tools, Machine Learning has become a tool for solving real world problems that is increasingly more accessible in many industrial and social sectors. With the growth in number of deployments, also grows the number of known challenges and hurdles that practitioners face along the deployment process to ensure the continual delivery of good performance from deployed Machine Learning systems. Such challenges can lie in adoption of ML algorithms to concrete use cases, discovery and quality of data, maintenance of production ML systems, as well as ethics.

Author Information

Alessandra Tosi (Mind Foundry)

Alessandra Tosi is a Machine Learning research scientist at Mind Foundry, an Oxford University spin out company. Her research interest falls in the area of probabilistic models, with a particular focus on Gaussian Process based techniques and latent variable models. She is interested in the underlying geometry of probabilistic models, with a special attention to the behaviour of metrics in Probabilistic Geometries. In her work a great attention is paid to data visualization and interpretability of these models.

Andrei Paleyes (Universtiy of Cambridge)
Christian Cabrera (University of Cambridge)
Fariba Yousefi (AstraZeneca)
S Roberts (University of Oxford)

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