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Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunities
[article]
2018
arXiv
pre-print
Development of machine learning (ML) workflows is a tedious process of iterative experimentation: developers repeatedly make changes to workflows until the desired accuracy is attained. We describe our vision for a "human-in-the-loop" ML system that accelerates this process: by intelligently tracking changes and intermediate results over time, such a system can enable rapid iteration, quick responsive feedback, introspection and debugging, and background execution and automation. We finally
arXiv:1804.05892v1
fatcat:pu2ywdlvj5dddjtpnf7ncpqgey