April 2026
Intermediate
586 pages
7h 41m
Chinese
Content preview from 学习 AutoML (Chinese Edition)
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第四部分：生产环境 与 MLOps
构建准确的模型只是成功的一半。另一半——通常也是更难的一半——在于将这些模型投入生产环境，确保其稳定运行，并保证它们在环境变化时仍能保持良好性能。许多 AutoML 项目正是在这一步失败的——不是因为模型质量问题，而是因为运营环节的缺失。
在本部分中，我们将弥合 AutoML 与 MLOps 之间的鸿沟。您将学习如何将 AutoML 集成到 MLflow 等现代机器学习平台中，使用 Apache Airflow 构建可靠的数据管道，并实施 CI/CD 实践，使模型部署与软件部署一样严格。这些并非理论上的最佳实践，而是来自每天处理数百万次预测的生产系统的实战验证方法。本部分最后将介绍在生产环境中部署和维护 AutoML 所需的运维模式。
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