May 2025
Intermediate to advanced
264 pages
3h 20m
Chinese
Overview
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如果您正在训练一个机器学习模型，但不确定如何将其投入生产，这本书将为您提供帮助。Kubeflow 为模型的生命周期的不同阶段提供了一系列云原生工具，从数据探索、特征准备、模型训练到模型服务。本指南帮助数据科学家使用 Kubeflow 构建生产级机器学习实现，并向数据工程师展示如何使模型具有可扩展性和可靠性。
本书作者 Holden Karau、Trevor Grant、Ilan Filonenko、Richard Liu 和 Boris Lublinsky 通过本书中的实例，解释了如何使用 Kubeflow 在云中的 Kubernetes 或本地开发环境中训练和提供机器学习模型。
- 了解 Kubeflow 的设计、核心组件及其解决的问题
- 了解不同集群类型上的 Kubeflow 之间的差异
- 使用 Kubeflow 与 Scikit-learn、TensorFlow 和 Apache Spark 等流行工具训练模型
- 使用 Kubeflow Pipelines 保持模型更新
- 了解如何捕获模型训练元数据
- 探索如何使用其他开源工具扩展 Kubeflow
- 使用超参数调优进行训练
- 学习如何在生产环境中部署模型
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