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...
| Main Authors: | , |
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| Format: | eBook |
| Language: | English |
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O'Reilly Media, Inc.
2020
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| Edition: | 1st edition |
| Subjects: | |
| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
| Summary: | 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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| Item Description: | Mode of access: World Wide Web Made available through: Safari, an O'Reilly Media Company |
| Physical Description: | 1 video file, approximately 42 min. |