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3.6c-.38-.34-.96-.34-1.34 0l-8.36 7.53c-.34.3-.13.87.33.87H5v7c0 .55.45 1 1 1h3c.55 0 1-.45 1-1z" fill="currentColor"></path></svg></a></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item"><a class="breadcrumbs__link" itemprop="item" href="/docs/category/features"><span itemprop="name">Features</span></a><meta itemprop="position" content="1"></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item"><a class="breadcrumbs__link" itemprop="item" href="/docs/managed-datahub/observe/assertions"><span itemprop="name">Assertions (Data Quality)</span></a><meta itemprop="position" content="2"></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item breadcrumbs__item--active"><span class="breadcrumbs__link" itemprop="name">Smart Assertions ⚡ (Anomaly Detection)</span><meta itemprop="position" content="3"></li></ul></nav><span class="theme-doc-version-badge badge badge--secondary">Version: Next</span><div class="tocCollapsible_ETCw theme-doc-toc-mobile tocMobile_ITEo"><button type="button" class="clean-btn tocCollapsibleButton_TO0P">On this page</button></div><div class="theme-doc-markdown markdown"><h1>Smart Assertions (AI Anomaly Detection) ⚡</h1><div class="availabilityCard_P5od card"><strong>Feature Availability</strong><div><span class="platform_wqXv">Self-Hosted DataHub <span role="img" aria-label="close-circle" class="anticon anticon-close-circle"><svg fill-rule="evenodd" viewBox="64 64 896 896" focusable="false" data-icon="close-circle" width="1em" height="1em" fill="currentColor" aria-hidden="true"><path d="M512 64c247.4 0 448 200.6 448 448S759.4 960 512 960 64 759.4 64 512 264.6 64 512 64zm127.98 274.82h-.04l-.08.06L512 466.75 384.14 338.88c-.04-.05-.06-.06-.08-.06a.12.12 0 00-.07 0c-.03 0-.05.01-.09.05l-45.02 45.02a.2.2 0 00-.05.09.12.12 0 000 .07v.02a.27.27 0 00.06.06L466.75 512 338.88 639.86c-.05.04-.06.06-.06.08a.12.12 0 000 .07c0 .03.01.05.05.09l45.02 45.02a.2.2 0 00.09.05.12.12 0 00.07 0c.02 0 .04-.01.08-.05L512 557.25l127.86 127.87c.04.04.06.05.08.05a.12.12 0 00.07 0c.03 0 .05-.01.09-.05l45.02-45.02a.2.2 0 00.05-.09.12.12 0 000-.07v-.02a.27.27 0 00-.05-.06L557.25 512l127.87-127.86c.04-.04.05-.06.05-.08a.12.12 0 000-.07c0-.03-.01-.05-.05-.09l-45.02-45.02a.2.2 0 00-.09-.05.12.12 0 00-.07 0z"></path></svg></span></span></div><div><span role="img" aria-label="cloud" class="anticon anticon-cloud managedIcon_AxXO"><svg viewBox="64 64 896 896" focusable="false" data-icon="cloud" width="1em" height="1em" fill="currentColor" aria-hidden="true"><path d="M811.4 418.7C765.6 297.9 648.9 212 512.2 212S258.8 297.8 213 418.6C127.3 441.1 64 519.1 64 612c0 110.5 89.5 200 199.9 200h496.2C870.5 812 960 722.5 960 612c0-92.7-63.1-170.7-148.6-193.3zm36.3 281a123.07 123.07 0 01-87.6 36.3H263.9c-33.1 0-64.2-12.9-87.6-36.3A123.3 123.3 0 01140 612c0-28 9.1-54.3 26.2-76.3a125.7 125.7 0 0166.1-43.7l37.9-9.9 13.9-36.6c8.6-22.8 20.6-44.1 35.7-63.4a245.6 245.6 0 0152.4-49.9c41.1-28.9 89.5-44.2 140-44.2s98.9 15.3 140 44.2c19.9 14 37.5 30.8 52.4 49.9 15.1 19.3 27.1 40.7 35.7 63.4l13.8 36.5 37.8 10c54.3 14.5 92.1 63.8 92.1 120 0 33.1-12.9 64.3-36.3 87.7z"></path></svg></span><span class="platform_wqXv platformAvailable_Y8lN">DataHub Cloud <span role="img" aria-label="check-circle" class="anticon anticon-check-circle"><svg viewBox="64 64 896 896" focusable="false" data-icon="check-circle" width="1em" height="1em" fill="currentColor" aria-hidden="true"><path d="M512 64C264.6 64 64 264.6 64 512s200.6 448 448 448 448-200.6 448-448S759.4 64 512 64zm193.5 301.7l-210.6 292a31.8 31.8 0 01-51.7 0L318.5 484.9c-3.8-5.3 0-12.7 6.5-12.7h46.9c10.2 0 19.9 4.9 25.9 13.3l71.2 98.8 157.2-218c6-8.3 15.6-13.3 25.9-13.3H699c6.5 0 10.3 7.4 6.5 12.7z"></path></svg></span></span></div></div><h2 class="anchor anchorWithStickyNavbar_LWe7" id="what-are-smart-assertions">What are Smart Assertions?<a href="#what-are-smart-assertions" class="hash-link" aria-label="Direct link to What are Smart Assertions?" title="Direct link to What are Smart Assertions?"></a></h2><p>Smart Assertions are Anomaly Detection monitors that will train on the historical patterns of the data, and make predictions of what 'normal' looks like. They are powered by an incredibly sophisticated ML pipeline, enabling them to account for a large variety of trends in the data, including seasonality.</p><p align="left"><img loading="lazy" width="80%" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/observe/shared/smart-assertion-example.png" class="img_ev3q"></p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="how-do-i-create-smart-assertions">How do I create Smart Assertions?<a href="#how-do-i-create-smart-assertions" class="hash-link" aria-label="Direct link to How do I create Smart Assertions?" title="Direct link to How do I create Smart Assertions?"></a></h2><p>Today, you can create Smart Assertions for 3 types of assertions. To learn more about each one, click the respective links below:</p><ol><li><a href="/docs/managed-datahub/observe/volume-assertions#anomaly-detection-with-smart-assertions-">Volume</a></li><li><a href="/docs/managed-datahub/observe/freshness-assertions#anomaly-detection-with-smart-assertions-">Freshness</a></li><li><a href="/docs/managed-datahub/observe/column-assertions#anomaly-detection-with-smart-assertions-">Column Metrics</a></li></ol><h2 class="anchor anchorWithStickyNavbar_LWe7" id="improving-smart-assertion-quality">Improving Smart assertion quality<a href="#improving-smart-assertion-quality" class="hash-link" aria-label="Direct link to Improving Smart assertion quality" title="Direct link to Improving Smart assertion quality"></a></h2><p>You can improve predictions through two key levers:</p><ol><li>Tuning</li><li>Anomaly feedback</li></ol><h3 class="anchor anchorWithStickyNavbar_LWe7" id="tuning">Tuning<a href="#tuning" class="hash-link" aria-label="Direct link to Tuning" title="Direct link to Tuning"></a></h3><p>To tune smart assertion predictions, you have 3 key knobs available to you. Each of these can be accessed in the <strong>Settings</strong> tab of the Assertion.</p><p align="left"><img loading="lazy" width="80%" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/observe/shared/smart-assertion-tuning.png" class="img_ev3q"></p><p><strong>Sensitivity</strong>
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A higher sensitivity will have a tighter fit on the data. A lower sensitivity will allow for more data variation before an anomaly is flagged.</p><p><strong>Exclusion Windows</strong>
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Set time windows to exclude from training data. This can be useful to exclude known maintenance windows or other periods of downtime, seasonal spikes e.g. holidays or any other windows that are not representative of normal data trends.</p><p><strong>Training data lookback window</strong>
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This is the number of days our ML models will look back to gather training data to generate predictions. If this is too large, we may pick up on old data that is no longer part of the current trend. If it is too short, we may miss key seasonal patterns. You can leverage this alongside exclusion windows to improve the quality of predictions.</p><h3 class="anchor anchorWithStickyNavbar_LWe7" id="anomaly-feedback">Anomaly Feedback<a href="#anomaly-feedback" class="hash-link" aria-label="Direct link to Anomaly Feedback" title="Direct link to Anomaly Feedback"></a></h3><h4 class="anchor anchorWithStickyNavbar_LWe7" id="false-alarms">False alarms<a href="#false-alarms" class="hash-link" aria-label="Direct link to False alarms" title="Direct link to False alarms"></a></h4><p>When an anomaly is flagged by the smart assertion, you may hover over the result dot and select <code>Mark as Normal</code>. This will include this data point in the training set, and the model will adjust to ensure it does not flag such data points as anomalies again.</p><p><strong>However</strong>, there are some cases where anomalies are actually expected. For instance, if this is a sudden jump in data, <strong>but it will be the new normal moving forward</strong>, we recommend selecting the <code>Train as new Normal</code> option. This will add an exclusion window to all data prior to this run event, and the smart assertion will begin training on data points from this point forward.</p><h4 class="anchor anchorWithStickyNavbar_LWe7" id="missed-alarms">Missed alarms<a href="#missed-alarms" class="hash-link" aria-label="Direct link to Missed alarms" title="Direct link to Missed alarms"></a></h4><p>If an anomaly is not caught by our Smart Assertions, we recommend doing a few things:</p><ol><li>You can click <code>Mark as Anomaly</code> to flag this specific data point as an anomaly. This will exclude that data point from the training data.</li><li>You should look back at the historical stats on the <code>Stats</code> tab of the table, and see if there were any periods of anomalous data that may be polluting the training set. If so, add an <code>Exclusion Window</code> in the <strong>Settings tab</strong> of the assertion, to remove this polluted period of data from the training data.</li><li>Finally, consider increasing the sensitivity of the assertion in the <strong>Settings tab</strong> which will reduce the range of allowable values.</li></ol><p align="left"><img loading="lazy" width="80%" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/observe/shared/smart-assertion-feedback.png" class="img_ev3q"></p></div><footer class="theme-doc-footer docusaurus-mt-lg"><div class="slackUtm_uoBr"><div class="slackUtm_uoBr"><hr>Need more help? 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toc-highlight">What are Smart Assertions?</a></li><li><a href="#how-do-i-create-smart-assertions" class="table-of-contents__link toc-highlight">How do I create Smart Assertions?</a></li><li><a href="#improving-smart-assertion-quality" class="table-of-contents__link toc-highlight">Improving Smart assertion quality</a><ul><li><a href="#tuning" class="table-of-contents__link toc-highlight">Tuning</a></li><li><a href="#anomaly-feedback" class="table-of-contents__link toc-highlight">Anomaly Feedback</a></li></ul></li></ul></div></div></div></div></main></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="row footer__links"><div class="col footer__col"><div class="footer__title">Docs</div><ul class="footer__items clean-list"><li class="footer__item"><a class="footer__link-item" href="/docs/">Introduction</a></li><li class="footer__item"><a class="footer__link-item" href="/docs/quickstart">Quickstart</a></li></ul></div><div class="col 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