TFX Production ML pipelines with TensorFlow
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. Robert Crowe and Charles Chen (Google) takes a deep dive in...
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| Format: | Online |
| Sprache: | eng |
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O'Reilly Media, Inc.
2020
Sebastopol, CA O'Reilly Media Inc. |
| Ausgabe: | 1st edition |
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| Online Zugang: | https://learning.oreilly.com/library/view/-/0636920373865/?ar |
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Tag hinzufügen | Zusammenfassung: | 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. Robert Crowe and Charles Chen (Google) takes a deep dive into TensorFlow Extended (TFX), the open source version of the ML infrastructure platform that Google has developed for its own production ML pipelines. Prerequisite knowledge Experience with ML development and software development What you'll learn Discover issues and best practices for putting machine learning models and applications into production... |
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| Beschreibung: | 1 Online-Ressource (1 video file, approximately 42 min.) |