Monitoring on 中文AI术语词典https://terms-en.ai-term-hub.com/zh/tags/monitoring/Recent content in Monitoring on 中文AI术语词典Hugozh-cnSat, 18 Jul 2026 11:44:45 +0000AI可观测性https://terms-en.ai-term-hub.com/zh/terms/ai_observability/Sat, 18 Jul 2026 11:03:04 +0000https://terms-en.ai-term-hub.com/zh/terms/ai_observability/<h2 id="definition">Definition</h2> <p>AI可观测性将传统的软件监控扩展到解决机器学习系统的独特挑战。它涉及实时跟踪模型性能、数据漂移和推理延迟，从而帮助开发者深入理解黑盒模型的决策过程及其在生产环境中的行为。</p> <h3 id="summary">Summary</h3> <p>通过日志、指标和追踪来监控和理解机器学习系统内部状态的做法。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>数据漂移</li> <li>模型监控</li> <li>遥测</li> <li>调试</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>检测生产模型中的概念漂移</li> <li>排查低置信度预测问题</li> <li>确保AI服务符合服务等级协议(SLA)</li> </ul> <h2 id="code-example">Code Example</h2> <div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-python" data-lang="python"><span style="display:flex;"><span><span style="color:#f92672">import</span> mlflow </span></span><span style="display:flex;"><span> </span></span><span style="display:flex;"><span><span style="color:#75715e"># Log metrics during training</span> </span></span><span style="display:flex;"><span>mlflow<span style="color:#f92672">.</span>log_metric(<span style="color:#e6db74">&#39;accuracy&#39;</span>, <span style="color:#ae81ff">0.95</span>) </span></span><span style="display:flex;"><span>mlflow<span style="color:#f92672">.</span>log_metric(<span style="color:#e6db74">&#39;loss&#39;</span>, <span style="color:#ae81ff">0.05</span>) </span></span><span style="display:flex;"><span> </span></span><span style="display:flex;"><span><span style="color:#75715e"># Track model parameters</span> </span></span><span style="display:flex;"><span>mlflow<span style="color:#f92672">.</span>log_param(<span style="color:#e6db74">&#39;learning_rate&#39;</span>, <span style="color:#ae81ff">0.01</span>) </span></span><span style="display:flex;"><span>mlflow<span style="color:#f92672">.</span>log_param(<span style="color:#e6db74">&#39;epochs&#39;</span>, <span style="color:#ae81ff">10</span>) </span></span></code></pre></div><h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/mlops-%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%90%E7%BB%B4/">MLOps (机器学习运维)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E6%A8%A1%E5%9E%8B%E6%BC%82%E7%A7%BB-model-drift/">模型漂移 (Model Drift)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E7%B3%BB%E7%BB%9F%E7%9B%91%E6%8E%A7-system-monitoring/">系统监控 (System Monitoring)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E9%81%A5%E6%B5%8B-telemetry/">遥测 (Telemetry)</a></li> </ul>图表/图结构https://terms-en.ai-term-hub.com/zh/terms/graphs/Sat, 18 Jul 2026 10:51:41 +0000https://terms-en.ai-term-hub.com/zh/terms/graphs/<h2 id="definition">Definition</h2> <p>虽然单数“图”指抽象的数据结构，但“graphs”通常指多个不同的图实例，或在机器学习监控中使用的可视化图表。在可视化中，包括折线图或条形图等。</p> <h3 id="summary">Summary</h3> <p>复数形式，指多个图结构或机器学习中的数据趋势可视化表示。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>多个结构</li> <li>可视化</li> <li>指标绘图</li> <li>数据集合</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>训练损失可视化</li> <li>多图数据集</li> <li>性能基准测试图表</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/plot-%E7%BB%98%E5%9B%BE/">Plot (绘图)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/chart-%E5%9B%BE%E8%A1%A8/">Chart (图表)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/gnn-%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/">GNN (图神经网络)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/visualization-%E5%8F%AF%E8%A7%86%E5%8C%96/">Visualization (可视化)</a></li> </ul>