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Advanced model deployments with TensorFlow serving /
"TensorFlow Serving is one of the cornerstones in the TensorFlow ecosystem. It has eased the deployment of machine learning models tremendously and led to an acceleration of model deployments. Unfortunately, machine learning engineers aren't familiar with the details of TensorFlow Serving,...
| Clasificación: | Libro Electrónico |
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| Autor Corporativo: | |
| Formato: | Electrónico Congresos, conferencias Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
O'Reilly Media,
2020.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | "TensorFlow Serving is one of the cornerstones in the TensorFlow ecosystem. It has eased the deployment of machine learning models tremendously and led to an acceleration of model deployments. Unfortunately, machine learning engineers aren't familiar with the details of TensorFlow Serving, and they're missing out on significant performance increases. Hannes Hapke (SAP ConcurLabs) provides a brief introduction to TensorFlow Serving, then leads a deep dive into advanced settings and use cases. He introduces advanced concepts and implementation suggestions to increase the performance of the TensorFlow Serving setup, which includes an introduction to how clients can request model meta-information from the model server, an overview of model optimization options for optimal prediction throughput, an introduction to batching requests to improve the throughput performance, an example implementation to support model A/B testing, and an overview of monitoring your TensorFlow Serving setup."--Resource description page |
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| Notas: | Title from resource description page (viewed July 21, 2020). This session is from the 2019 O'Reilly TensorFlow World Conference in Santa Clara, CA. |
| Descripción Física: | 1 online resource (1 streaming video file (41 min., 22 sec.)) |