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嵌入表管理
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<a href="#_20" class="md-nav__link">
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公平性和偏见
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<article class="md-content__inner md-typeset">
<h1 id="ml">ML系统设计<a class="headerlink" href="#ml" title="Permanent link">&para;</a></h1>
<p><em>ML系统设计将文件01-03中的基础设施模式应用于机器学习的特定挑战。本文件涵盖ML生命周期、数据管理、训练基础设施、模型评估、服务策略、特征工程、ML流水线和监控</em></p>
<ul>
<li>像"为YouTube设计一个推荐系统"这样的系统设计面试问题并不是要求你描述推荐算法。它要求你设计<strong>整个系统</strong>：数据流水线、特征工程、模型训练、评估、服务、监控和迭代。本文件提供了框架。</li>
</ul>
<h2 id="ml_1">ML系统生命周期<a class="headerlink" href="#ml_1" title="Permanent link">&para;</a></h2>
<p><img alt="ML系统生命周期：从问题定义到部署和监控，持续迭代" src="../../images/ml_lifecycle.svg" /></p>
<ul>
<li>每个ML系统都遵循相同的生命周期，无论是垃圾邮件分类器还是基础模型：</li>
</ul>
<div class="highlight"><pre><span></span><code><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>问题定义 → 数据 → 特征 → 训练 → 评估 → 部署 → 监控 → 迭代
<a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a> ↑ │
<a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a> └────────────────────────────────────────────────────────┘
</code></pre></div>
<h3 id="_1">问题定义<a class="headerlink" href="#_1" title="Permanent link">&para;</a></h3>
<ul>
<li>在接触数据或模型之前，先定义：<ul>
<li><strong>预测什么？</strong>（点击概率、下一个令牌、目标边界框）</li>
<li><strong>用户是谁？</strong>（最终用户、内部分析师、其他ML模型）</li>
<li><strong>约束是什么？</strong>（延迟&lt;100ms、离线批量操作可以、必须在设备上运行）</li>
<li><strong>业务指标是什么？</strong>（收入、参与度、准确率）以及ML指标如何与之关联？</li>
<li><strong>基线是什么？</strong>（启发式方法、基于规则的系统、现有模型）——你必须击败它才能证明ML系统的价值。</li>
</ul>
</li>
<li><strong>常见错误</strong>：在理解问题之前直接跳到模型架构。"我们应该使用Transformer"不是系统设计的答案。"我们需要在200ms内预测1000万个候选的点击概率，因此我们需要一个两阶段系统：快速检索然后一个小型排序模型"才是。</li>
</ul>
<h2 id="_2">数据管理<a class="headerlink" href="#_2" title="Permanent link">&para;</a></h2>
<h3 id="_3">数据收集和标注<a class="headerlink" href="#_3" title="Permanent link">&para;</a></h3>
<ul>
<li><strong>显式标签</strong>：人类标注数据（点击/不点击、目标边界框、对话质量评分）。昂贵（取决于复杂度，每个标签约<span class="arithmatex">\(0.02-\)</span>10）、缓慢且主观。</li>
<li><strong>隐式标签</strong>：从用户行为中推导标签。点击、停留时间、购买、跳过。廉价且丰富，但有噪声（点击不意味着满意；跳过不意味着不喜欢）。</li>
<li><strong>程序化标注</strong>（Snorkel）：编写标注函数（启发式方法、正则表达式、现有模型），对每个样本进行投票。统计汇总投票以产生概率标签。可扩展到数百万样本，具有中等准确度。</li>
<li><strong>主动学习</strong>：模型识别最不确定的样本，并请求人工标注这些样本。这最大限度地提高了标注效率：1000个主动选择的标签可以匹配10000个随机标签的质量。</li>
</ul>
<h3 id="_4">数据质量<a class="headerlink" href="#_4" title="Permanent link">&para;</a></h3>
<ul>
<li><strong>数据验证</strong>：检查每批传入数据的模式违反（字段缺失、类型错误）、分布偏移（平均值显著变化）和数量异常（预期100万行，收到50万行）。</li>
<li><strong>Great Expectations</strong>和<strong>TFX Data Validation</strong>是定义数据期望并在违反时发出告警的工具。</li>
<li><strong>数据版本管理</strong>：每次训练运行应该是可重现的。<strong>DVC</strong>（第15章）将数据文件与代码一起追踪。每个数据集版本获得一个哈希值；训练配置引用该哈希值。</li>
</ul>
<h3 id="_5">特征存储<a class="headerlink" href="#_5" title="Permanent link">&para;</a></h3>
<p><img alt="特征存储：相同的特征计算同时供给离线存储（用于训练）和在线存储（用于服务），防止训练-服务偏差" src="../../images/feature_store.svg" /></p>
<ul>
<li><strong>特征存储</strong>（第15章）为训练和服务提供一致的特征。关键概念：<ul>
<li><strong>离线特征</strong>：从批处理流水线（Spark）计算，存储在数据仓库中。在训练和批量推理期间使用。示例：用户过去30天的平均会话时长、商品的总购买次数。</li>
<li><strong>在线特征</strong>：实时计算或预先计算并从低延迟存储（Redis、DynamoDB）提供服务。在实时推理期间使用。示例：用户最近的5个操作、当前购物车内容。</li>
<li><strong>训练-服务偏差</strong>：如果特征计算在训练和服务之间不同，模型在推理时看到的特征值与训练时不同。特征存储通过对两者使用相同的计算来消除此问题。</li>
</ul>
</li>
</ul>
<h2 id="_6">训练基础设施<a class="headerlink" href="#_6" title="Permanent link">&para;</a></h2>
<ul>
<li>对于本书的读者，分布式训练在第6章（数据并行、模型并行、混合精度、缩放定律）中已有深入介绍。这里我们关注<strong>系统</strong>方面：</li>
<li><strong>实验跟踪</strong>（W&amp;B、MLflow——第15章）：每次训练运行记录超参数、指标、git提交、数据版本和硬件。这是模型版本控制的ML等价物。</li>
<li><strong>超参数调优</strong>：自动化搜索超参数。方法：网格搜索（穷尽，昂贵）、随机搜索（出奇地有效）、贝叶斯优化（对目标建模，在改进可能性高的地方采样）和<strong>ASHA</strong>（异步连续减半：启动许多试验，早期淘汰表现不佳的）。</li>
<li><strong>训练流水线编排</strong>（Airflow、Kubeflow——第15章）：自动化数据准备→训练→评估→注册的序列。安排每日重新训练。在失败时发出告警。</li>
</ul>
<h2 id="_7">模型评估<a class="headerlink" href="#_7" title="Permanent link">&para;</a></h2>
<h3 id="_8">离线评估<a class="headerlink" href="#_8" title="Permanent link">&para;</a></h3>
<ul>
<li><strong>保留测试集</strong>：在模型训练时从未见过的数据上评估。标准做法，但如果测试集不代表生产数据，可能会产生误导。</li>
<li><strong>基于分片的评估</strong>：在子组上评估（按用户人口统计、内容类型、语言、时间段）。一个总体准确率95%的模型可能在特定少数群体上的准确率只有70%——不可接受。</li>
<li><strong>回测</strong>：对于时间序列或顺序预测，按时间顺序在历史数据上进行评估。使用截至时间<span class="arithmatex">\(t\)</span>的数据训练，在<span class="arithmatex">\(t\)</span>到<span class="arithmatex">\(t + \Delta t\)</span>的数据上评估。避免使用未来数据进行训练导致的数据泄露。</li>
</ul>
<h3 id="_9">在线评估<a class="headerlink" href="#_9" title="Permanent link">&para;</a></h3>
<p><img alt="A/B测试：将用户随机分为对照组（旧模型）和实验组（新模型），以统计显著性比较指标" src="../../images/ab_testing.svg" /></p>
<ul>
<li><strong>A/B测试</strong>：将实时流量随机分为对照组（旧模型）和实验组（新模型）。以统计显著性比较业务指标（收入、参与度、留存率）。评估ML变更的黄金标准。<ul>
<li><strong>样本量</strong>：你需要足够的数据来检测预期的效应量。点击率0.1%的改进需要数百万次展示才能以显著性检测到。</li>
<li><strong>时长</strong>：运行至少一个完整周期（大多数产品1-2周）以捕获日-周效应。</li>
<li><strong>护栏指标</strong>：监控不应变化的指标（页面加载时间、错误率、崩溃率）以及目标指标。一个增加收入但同时增加崩溃率的模型是净负面的。</li>
</ul>
</li>
<li><strong>影子部署</strong>：在生产中与新模型并行运行旧模型。两者接收相同的请求，但只有旧模型的预测会提供给用户。比较输出。这能在不影响用户的情况下捕获bug和质量问题。</li>
<li><strong>交错实验</strong>：对于排序问题，将旧模型和新模型的结果交错在一个列表中。用户与交错列表交互，你测量哪个模型的结果获得更多参与。相比A/B测试需要更少的用户即可达到显著性。</li>
</ul>
<h2 id="_10">模型服务<a class="headerlink" href="#_10" title="Permanent link">&para;</a></h2>
<h3 id="vs">批量vs实时<a class="headerlink" href="#vs" title="Permanent link">&para;</a></h3>
<ul>
<li><strong>批量推理</strong>：预先计算所有可能输入的预测结果。存储在数据库/缓存中。从缓存提供服务。适用于：输入空间有限（每晚为所有用户推荐）、新鲜度不重要（每日预测即可）、延迟容忍度高。</li>
<li><strong>实时推理</strong>：按需为每个请求计算预测结果。适用于：输入空间无限（任何用户查询）、新鲜度重要（立即为此特定查询进行预测）、延迟必须低。</li>
<li>许多系统<strong>两者都用</strong>：批量预计算一组候选结果（便宜，覆盖80%的流量），实时处理其余部分（昂贵，覆盖尾部查询和新用户）。</li>
</ul>
<h3 id="_11">模型版本管理和注册表<a class="headerlink" href="#_11" title="Permanent link">&para;</a></h3>
<ul>
<li><strong>模型注册表</strong>（MLflow、W&amp;B、SageMaker）存储训练好的模型及其元数据：<ul>
<li>版本号和训练日期。</li>
<li>训练配置和数据版本。</li>
<li>评估指标（准确率、延迟、内存使用）。</li>
<li>阶段：开发→预发布→生产→归档。</li>
</ul>
</li>
<li><strong>回滚</strong>：如果新模型在生产中导致指标下降，立即恢复到前一个版本。注册表使这成为一键操作。</li>
</ul>
<h2 id="_12">特征工程<a class="headerlink" href="#_12" title="Permanent link">&para;</a></h2>
<ul>
<li><strong>特征工程</strong>将原始数据转换为模型所需的输入。它通常是ML中杠杆率最高的活动：更好的特征能改进每个模型，而更好的模型受限于它们收到的特征。</li>
</ul>
<h3 id="vs_1">在线vs离线特征<a class="headerlink" href="#vs_1" title="Permanent link">&para;</a></h3>
<ul>
<li><strong>离线特征</strong>是预先计算的，变化缓慢（用户人口统计、30天聚合）。由批处理流水线（Spark）计算，存储在特征存储中。</li>
<li><strong>在线特征</strong>反映当前状态，变化迅速（购物车中的商品、最近操作、当前位置）。从事件流实时计算或从快速存储中查找。</li>
<li><strong>特征新鲜度</strong>：某些特征需要秒级新鲜度（欺诈检测：此交易相对于最近5笔交易是否异常？）。其他的可以容忍小时级陈旧度（推荐：根据用户历史，该用户偏好什么类型？）。更新鲜的特征计算和服务更昂贵。</li>
</ul>
<h3 id="_13">常见特征模式<a class="headerlink" href="#_13" title="Permanent link">&para;</a></h3>
<ul>
<li><strong>计数特征</strong>：时间窗口内的事件计数（过去7天的购买次数、过去24小时的登录次数）。</li>
<li><strong>嵌入特征</strong>：分类变量的学习嵌入（用户嵌入、商品嵌入、查询嵌入）。这些是双塔模型和类似架构的输入。</li>
<li><strong>交叉特征</strong>：两个或多个特征的组合（user_age × item_category）。捕获单个特征无法捕获的交互。</li>
<li><strong>时间特征</strong>：自上次操作以来的时间、星期几、一天中的小时。捕获时间模式。</li>
<li><strong>聚合特征</strong>：数值特征在某个组上的均值、中位数、最小值、最大值、标准差（此卖家的商品平均评分）。</li>
</ul>
<h2 id="ml_2">ML流水线<a class="headerlink" href="#ml_2" title="Permanent link">&para;</a></h2>
<ul>
<li>ML流水线编排从数据到部署模型的整个工作流程：</li>
</ul>
<div class="highlight"><pre><span></span><code><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>数据摄入 → 验证 → 特征工程 → 训练 → 评估 → 注册 → 部署 → 监控
</code></pre></div>
<ul>
<li>每个步骤是编排器（Airflow、Kubeflow、Metaflow——第15章）中的一个任务。流水线：<ul>
<li>按计划运行（每日重新训练）或触发运行（新数据可用）。</li>
<li>是幂等的（重新运行产生相同结果）。</li>
<li>有重试逻辑（如果特征计算失败，使用退避重试3次）。</li>
<li>产生制品（训练好的模型、评估报告、特征统计），这些制品被版本化管理并存储。</li>
</ul>
</li>
<li><strong>Metaflow</strong>（Netflix/Outerbounds）特别适合ML：它对代码、数据和模型一起进行版本管理，支持相同代码的本地开发和云执行，并与K8s和AWS集成。</li>
</ul>
<h2 id="_14">监控<a class="headerlink" href="#_14" title="Permanent link">&para;</a></h2>
<ul>
<li>我们在第15章（Prometheus、Grafana、告警）中介绍了监控基础。这里我们关注<strong>ML特定的监控</strong>：</li>
</ul>
<h3 id="_15">数据漂移<a class="headerlink" href="#_15" title="Permanent link">&para;</a></h3>
<ul>
<li><strong>数据漂移</strong>发生在传入数据的分布相对于训练数据发生变化时。在夏季数据上训练的模型可能在冬季数据上表现不佳（不同的用户行为、不同的产品可用性）。</li>
<li><strong>检测</strong>：使用统计测试比较传入特征分布与训练分布：<ul>
<li><strong>KS检验</strong>（Kolmogorov-Smirnov）：比较两个经验分布。检验它们是否来自相同的底层分布。</li>
<li><strong>PSI</strong>（总体稳定性指数）：衡量分布偏移了多少。PSI &lt; 0.1为稳定，0.1-0.25为中度偏移，&gt; 0.25为显著偏移。</li>
<li><strong>嵌入漂移</strong>：使用质心距离或MMD（最大均值差异）比较传入查询的嵌入分布与训练集。</li>
</ul>
</li>
</ul>
<h3 id="_16">概念漂移<a class="headerlink" href="#_16" title="Permanent link">&para;</a></h3>
<ul>
<li><strong>概念漂移</strong>发生在输入和输出之间的关系发生变化时。特征看起来相同，但正确的预测不同。示例：用户偏好在一场文化活动、流行病或产品变更后发生转变。</li>
<li>概念漂移比数据漂移更难检测，因为它需要带标签的数据。监控代理指标：点击率、转化率、用户满意度评分。持续下降可能表明概念漂移。</li>
</ul>
<h3 id="_17">模型退化<a class="headerlink" href="#_17" title="Permanent link">&para;</a></h3>
<ul>
<li>模型会因多种原因随时间退化：数据漂移、概念漂移、特征流水线错误（特征开始返回空值）以及上游数据变化（第三方API更改其响应格式）。</li>
<li><strong>响应</strong>：检测到退化时，行动取决于严重程度：<ul>
<li>轻度：在最近数据上重新训练（定时重新训练可处理此情况）。</li>
<li>中度：调查根本原因（哪个特征发生了变化？哪个用户群体受到影响？）。</li>
<li>严重：立即回滚到以前的模型版本，然后调查。</li>
</ul>
</li>
</ul>
<h3 id="_18">反馈循环<a class="headerlink" href="#_18" title="Permanent link">&para;</a></h3>
<ul>
<li>ML系统创建<strong>反馈循环</strong>：模型的预测影响用户行为，后者成为下一个模型版本的训练数据。这些循环可能是良性的，也可能是恶性的。</li>
<li><strong>正反馈循环</strong>（危险的）：推荐模型主要展示热门商品→用户点击热门商品（因为他们只看到这些）→模型了解到热门商品更受欢迎→多样性崩溃。模型创造了确认其偏见的数据。</li>
<li><strong>负反馈循环</strong>（也危险的）：欺诈检测模型捕获了所有A类欺诈→没有A类欺诈进入训练数据→下一个模型未学会检测A类→A类欺诈重新出现。</li>
<li><strong>缓解措施</strong>：<ul>
<li><strong>探索</strong>：展示一些模型不确定的商品（epsilon-greedy、Thompson采样）。这生成了多样化的训练数据。</li>
<li><strong>反事实日志记录</strong>：记录模型<em>本会</em>预测的结果，而不仅仅是用户看到的结果。在反事实数据上训练以消除偏差。</li>
<li><strong>保留集</strong>：随机将一部分流量用于无模型过滤的服务。未经过滤的数据为评估模型质量提供了真实依据。</li>
<li><strong>延迟标签</strong>：在使用数据训练之前等待真实结果。今天点击的推荐可能明天就后悔。欺诈预测必须等待退款窗口（30-90天）。</li>
</ul>
</li>
</ul>
<h3 id="_19">嵌入表管理<a class="headerlink" href="#_19" title="Permanent link">&para;</a></h3>
<ul>
<li>大规模ML系统通常有包含1亿+条目的嵌入表（每个用户、商品、广告或实体一个嵌入）。大规模管理这些是系统挑战：</li>
<li><strong>存储</strong>：1亿实体×256维×float16 = 50 GB。不适合GPU内存。解决方案：存储在CPU内存中并配合GPU端缓存，跨多台机器分片，或使用<strong>哈希嵌入</strong>（将实体哈希到固定大小的表，接受冲突）。</li>
<li><strong>更新</strong>：嵌入随模型重新训练而变化。向服务部署新的嵌入表需要：在不中断实时流量时加载50 GB到内存，验证正确性，以及在指标下降时回滚。对嵌入表使用蓝绿部署。</li>
<li><strong>陈旧度</strong>：新创建的用户没有嵌入（冷启动问题）。解决方案：使用默认嵌入，通过特征到嵌入模型从用户特征派生嵌入，或回退到非个性化模型。</li>
</ul>
<h3 id="_20">公平性和偏见<a class="headerlink" href="#_20" title="Permanent link">&para;</a></h3>
<ul>
<li>ML系统可能会系统性地对待不同群体不同，通常反映了训练数据中的偏见。<strong>公平性监控</strong>是一种责任，不是可选功能。</li>
<li><strong>监控指标</strong>：<ul>
<li><strong>人口统计均等</strong>：不同群体（性别、种族、年龄）的正预测率是否不同？</li>
<li><strong>均等机会</strong>：不同群体的真阳性率是否不同？（招聘模型应该同样擅长识别所有群体的合格候选人。）</li>
<li><strong>校准</strong>：如果模型说P(合格) = 0.7对于群体A，那么群体A中实际上有70%是合格的吗？对于群体B也是同样？</li>
</ul>
</li>
<li><strong>实际步骤</strong>：<ul>
<li>在分片（子组）上评估模型性能，而不仅仅是总体指标。</li>
<li>在模型评估流水线中纳入公平性指标（一个提高总体准确率但降低特定群体性能的模型未经审查不应部署）。</li>
<li>记录已知的限制和失败模式。</li>
<li>为在敏感领域（招聘、贷款、刑事司法、医疗）部署的模型建立审查流程。</li>
</ul>
</li>
</ul>
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