We will introduce the technical challenges of putting machine learning models into production.
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Synopsis
In this talk, we will introduce the technical challenges of putting machine learning models into production including how to update live models and monitor those models to ensure they are behaving as expected.
We will illustrate these ideas with popular open source projects including Seldon Core and Kubeflow that focus on cloud-native solutions for these tasks. Secondly, as more companies are seeking to utilize machine learning within their business the ethical implications of automated business processes affecting society are becoming more apparent.
We will delve into some of the emerging ways to think about and manage these societal changes.
About the speakers
Clive Cox, CTO, Seldon
Clive is CTO at Seldon and has worked many years on scalable machine learning. He has a PhD in computational linguistics and studied speech & language processing at Cambridge University. He contributes to several popular open source projects including Seldon Core and Kubeflow’s KFServing.
Alejandro Saucedo, Engineering Director, Seldon and Chief Scientist at The Institute for Ethical ML
Alejandro is an engineering director at Seldon and has many years of software development experience. Throughout his career, Alejandro has held technical leadership positions across hyper-growth scale-ups and tech giants including Eigen Technologies and Bloomberg LP. He founded and runs the Institute for Ethical AI and Machine Learning.
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