Building machine learning pipelines automating model life cycles with TensorFlow
Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll lear...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Beijing :
O'Reilly
[2020]
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631708606719 |
Tabla de Contenidos:
- 1. Introduction
- 2. Introduction to TensorFlow extended
- 3. Data ingestion
- 4. Data validation
- 5. Data preprocessing
- 6. Model training
- 7. Model analysis and validation
- 8. Model deployment with TensorFlow serving
- 9. Advanced model deployments with TensorFlow serving
- 10. Advanced TensorFlow extended
- 11. Pipelines part 1: Beam and Apache airflow
- 12. Pipelines part 2: Kubeflow pipelines
- 13. Feedback loops
- 14. Data privacy for machine learning
- 15. The future of pipelines and next steps
- A. Introduction to infrastructure for machine learning
- B. Setting up a Kubernetes cluster on Google Cloud
- C. Tips for operating Kubeflow pipelines.
