April 2026
Intermediate
586 pages
7h 41m
Chinese
Overview
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学习 AutoML》是您在现实环境中应用自动化机器学习的实用指南。无论您是数据科学家、ML 工程师还是人工智能研究人员，本书都能帮助您超越实验，以更少的手动调整和更多的自动化来构建和部署高性能模型。使用 AutoGluon 作为主要工具包，您将学会如何构建、评估和部署 AutoML 模型，从而降低复杂性并加速创新。
作者 Kerem Tomak 分享了如何使用 Kubeflow、MLflow 和 Airflow 等流行工具将模型集成到端到端部署工作流中，同时探索 Vertex AI、SageMaker Autopilot、Azure AutoML、Auto-sklearn 和 H2O.ai 等跨平台方法。真实世界的案例研究重点介绍了金融、医疗保健和零售业的应用，而有关伦理、治理和代理人工智能的章节则有助于您的知识面向未来。
- 为表格、文本、图像和时间序列数据构建 AutoML 管道
- 利用 MLOps 最佳实践，通过快速、可扩展性的工作流部署模型
- 比较和浏览当今领先的 AutoML 平台
- 利用可解释性工具解释模型结果并做出明智决策
- 探索 AutoML 如何引领下一代代理人工智能系统
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