May 2025
Intermediate to advanced
380 pages
4h 56m
Chinese
Overview
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随着对扩展、实时访问和其他功能的需求，企业需要考虑构建运营机器学习管道。本实用指南可帮助您的公司将数据科学应用到各种现实的 MLOps 场景中。高级数据科学家、MLOps 工程师和机器学习工程师将学习如何应对阻碍许多企业将 ML 模型投入生产的挑战。
作者Yaron Haviv和Noah Gift采用以生产为中心的策略。而非从机器学习模型入手，您将学习如何设计持续运营管道，并确保各组件与实践可无缝融入其中。通过尽可能自动化组件，并使流程快速可重复，您的管道可随组织需求扩展。
您将学习如何在满足动态 MLOps 要求的同时，快速提供业务价值。本书将帮助您：
- 了解 MLOps 流程，包括其技术和商业价值
- 构建和构建有效的 MLOps 管道
- 在整个组织中高效扩展 MLOps
- 探索常见的 MLOps 用例
- 构建用于混合部署、实时预测和复合 AI 的 MLOps 管道
- 使用 LLMs 和生成式 AI 构建生产应用程序，同时降低风险、提高效率并微调模型
- 了解如何为 MLOps 的未来做好准备并适应未来
- 有效使用 HuggingFace 和 OpenAI 等预训练模型来补充您的 MLOps 策略
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