flykhan
2536c937e3
翻译自英文原版 maths-cs-ai-compendium，共 20 章全部完成。 第01章 向量 | 第02章 矩阵 | 第03章 微积分 第04章 统计学 | 第05章 概率论 | 第06章 机器学习 第07章 计算语言学 | 第08章 计算机视觉 | 第09章 音频与语音 第10章 多模态学习 | 第11章 自主系统 | 第12章 图神经网络 第13章 计算与操作系统 | 第14章 数据结构与算法 第15章 生产级软件工程 | 第16章 SIMD与GPU编程 第17章 AI推理 | 第18章 ML系统设计 第19章 应用人工智能 | 第20章 前沿人工智能 翻译说明： - 所有数学公式 $...$ / $$...$$、代码块、图片引用完整保留 - mkdocs.yml 配置中文导航 + language: zh - README.md 已翻译为中文（兼 docs/index.md） - docs/ 目录包含指向各章文件的 symlink - 约 29,000 行中文内容，排除 .cache/ 构建缓存
53 lines
4.4 KiB
XML
53 lines
4.4 KiB
XML
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<text x="350" y="22" text-anchor="middle" font-family="Arial, sans-serif" font-size="14" font-weight="bold" fill="#333">Feature Store: Same Features for Training and Serving</text>
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<!-- Feature computation -->
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<text x="90" y="82" text-anchor="middle" font-family="Arial, sans-serif" font-size="9" font-weight="bold" fill="#555">Raw Data</text>
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<text x="90" y="96" text-anchor="middle" font-family="Arial, sans-serif" font-size="8" fill="#555">events, logs, DBs</text>
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<!-- Feature engineering -->
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<text x="258" y="72" text-anchor="middle" font-family="Arial, sans-serif" font-size="9" font-weight="bold" fill="#f39c12">Feature</text>
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<text x="258" y="86" text-anchor="middle" font-family="Arial, sans-serif" font-size="9" font-weight="bold" fill="#f39c12">Engineering</text>
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<text x="258" y="102" text-anchor="middle" font-family="Arial, sans-serif" font-size="7" fill="#f39c12">same code for both paths</text>
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<!-- Split to offline and online -->
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<!-- Offline store -->
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<text x="471" y="55" text-anchor="middle" font-family="Arial, sans-serif" font-size="10" font-weight="bold" fill="#3498db">Offline Store</text>
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<text x="471" y="70" text-anchor="middle" font-family="Arial, sans-serif" font-size="8" fill="#3498db">data warehouse (batch)</text>
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<!-- Online store -->
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<!-- Training reads from offline -->
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<text x="628" y="55" text-anchor="middle" font-family="Arial, sans-serif" font-size="9" font-weight="bold" fill="#3498db">Training</text>
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<!-- Serving reads from online -->
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<text x="628" y="140" text-anchor="middle" font-family="Arial, sans-serif" font-size="9" font-weight="bold" fill="#e74c3c">Serving</text>
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<text x="350" y="210" text-anchor="middle" font-family="Arial, sans-serif" font-size="10" font-weight="bold" fill="#27ae60">Key: Same feature computation → No training-serving skew</text>
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<text x="350" y="228" text-anchor="middle" font-family="Arial, sans-serif" font-size="9" fill="#27ae60">Without a feature store: training computes user_age one way, serving computes it differently</text>
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<text x="350" y="240" text-anchor="middle" font-family="Arial, sans-serif" font-size="9" fill="#27ae60">→ model sees different features at inference → silently wrong predictions</text>
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