September 2025
Beginner to intermediate
210 pages
3h 1m
Chinese
Content preview from 高级Snowflake
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第 6 章 面向 AI 和 ML 的 Snowflake
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在本章中，我将带您了解 Snowflake 的 AI 生态系统，它为 Snowflake 平台带来了集成分析方面的重大进步。 该生态系统不断发展，现在可为 Snowflake 用户可能拥有的所有 AI 和 ML 用例提供解决方案。
在其中心，我们有 Snowflake Cortex，它是您在 Snowflake 中进入大型语言模型的网关。它包括由 Anthropic、Mistral、Reka、Meta 和 Google 等公司训练的 LLMs，以及 Snowflake 专有的 Snowflake Arctic（Snowflake 列出了所有可用模型）。Snowflake Cortex 为大型语言模型、向量处理和时间序列分析提供原生支持，所有这些都不需要数据移动或复杂的基础设施管理。 这使得复杂的生成式人工智能工作负载可以直接在您现有的数据架构中执行。 它与 Snowflake Notebooks 相辅相成，我们在前面的上手示例中看到了 Snowflake Notebooks。它提供了一个熟悉的基于 Jupyter 的环境，并增强了数据科学工作流的性能。它具有内置 Python 和 SQL 执行、简化的软件包管理以及与企业治理控制无缝集成的特点。
此外，为了实现快速开发，Snowflake 还提供了一个 Copilot。 这个智能助手可以根据你的工作负载和访问模式生成优化的 SQL、排除性能问题并提出改进建议，它还可以处理非结构化数据。 除此之外，Document AI 还提供自动索引和提取服务，通过自定义提取管道实现复杂的文档分析。 通过 Snowpark 容器服务支持自定义模型部署，通过模型注册表集成简化机器学习操作（MLOps），以及为生产型机器学习系统提供全面的特征存储功能，您一定会非常喜欢。 现在让我们详细介绍其中的一些功能，并探讨它们是如何工作的。
Snowflake 笔记本
将 Snowflake Notebooks 看作是您在雪视内部的数据科学工作台。它本质上是一个基于单元格的开发环境，您可以在其中混合使用 Python、SQL 和 Markdown，无论， ， ，它非常适合您在数据探索、模型构建和记录研究结果之间跳转的时刻，所有这些都集中在一个地方。Snowflake Notebooks 不仅允许您从 Snowflake 环境访问数据，还允许您将文件从本地机器上传到 Snowflake 并在笔记本中访问它们，同时保留从外部阶段（云存储）访问文件和从 Snowflake Marketplace 探索数据集的功能。
为了支持不同的工作负载，Snowflake Notebooks 支持两种不同的运行时：仓库环境和容器环境。表 6-1显示了两种运行时的比较。
| 支持的功能 | 仓库运行时 | 容器运行时 |
|---|---|---|
| 计算 | 内核在笔记本仓库上运行。 | 内核在计算池节点上运行。 |
| 运行环境 | Python 3.9 | Python 3.10（预览版）。 |
| 基础图像 | Streamlit + Snowpark | Snowflake Container Runtime（CPU 和 GPU 映像预装 Python 库）。 |
| 附加 Python 库 | 使用 Snowflake Anaconda 或从 Snowflake ... |
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