Interview
summary
Describes a production AI stack that automatically matches workers to gigs, prices work and predicts escalations by monitoring development lifecycle metrics and client–developer communications, and experiments with generative music modeling using deep learning. Claims deep models already peak in vision and text but need computationally tractable methods for massive datasets. Lists concrete ops problems: versioning datasets and models, storing huge trained models, live deployment, online evaluation and cheap rollback.
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chapters (5)
- 0:00Start
- 0:55What aspects of Al do you think work already?
- 1:49What is your key focus in Data, Machine Learning and Al?
- 2:29What do you like about the Al By the Bay Conference?
- 3:01DATA BY THE BAY by the Bay data.bythebay.io scale.bythebay.io Data and Scale By the Bay November 2017
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