ML development often focuses on metrics, delaying work on deployment and scaling issues. ML development designed for production deployments typically follows a pipeline model, with scaling and maintainability as inherent parts of the design. We examine TensorFlow Extended (TFX), the open source version of the ML infrastructure platform that Google has developed for its own production ML pipelines.
Presented by: Robert Crowe, Charles Chen
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