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By integrating Feature Store with DataHub, you can track the lineage of features used in AI/ML models, understand how features are generated, and how they are used to train models.</p><p>For technical details on feature store entities, please refer to the following docs:</p><ul><li><a href="/docs/generated/metamodel/entities/mlfeature">MlFeature</a></li><li><a href="/docs/generated/metamodel/entities/mlprimarykey">MlPrimaryKey</a></li><li><a href="/docs/generated/metamodel/entities/mlfeaturetable">MlFeatureTable</a></li></ul><h3 class="anchor anchorWithStickyNavbar_LWe7" id="goal-of-this-guide">Goal Of This Guide<a href="#goal-of-this-guide" class="hash-link" aria-label="Direct link to Goal Of This Guide" title="Direct link to Goal Of This Guide"></a></h3><p>This guide will show you how to</p><ul><li>Create feature store entities: MlFeature, MlFeatureTable, MlPrimaryKey</li><li>Read feature store entities: MlFeature, MlFeatureTable, MlPrimaryKey</li><li>Attach MlModel to MlFeature</li><li>Attach MlFeatures to MlFeatureTable</li><li>Attached MlFeatures to upstream Datasets that power them</li></ul><h2 class="anchor anchorWithStickyNavbar_LWe7" id="prerequisites">Prerequisites<a href="#prerequisites" class="hash-link" aria-label="Direct link to Prerequisites" title="Direct link to Prerequisites"></a></h2><p>For this tutorial, you need to deploy DataHub Quickstart and ingest sample data.
For detailed steps, please refer to <a href="/docs/quickstart">Datahub Quickstart Guide</a>.</p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="create-ml-entities">Create ML Entities<a href="#create-ml-entities" class="hash-link" aria-label="Direct link to Create ML Entities" title="Direct link to Create ML Entities"></a></h2><div class="theme-admonition theme-admonition-note alert alert--secondary admonition_LlT9"><div class="admonitionHeading_tbUL"><span class="admonitionIcon_kALy"><svg viewBox="0 0 14 16"><path fill-rule="evenodd" d="M6.3 5.69a.942.942 0 0 1-.28-.7c0-.28.09-.52.28-.7.19-.18.42-.28.7-.28.28 0 .52.09.7.28.18.19.28.42.28.7 0 .28-.09.52-.28.7a1 1 0 0 1-.7.3c-.28 0-.52-.11-.7-.3zM8 7.99c-.02-.25-.11-.48-.31-.69-.2-.19-.42-.3-.69-.31H6c-.27.02-.48.13-.69.31-.2.2-.3.44-.31.69h1v3c.02.27.11.5.31.69.2.2.42.31.69.31h1c.27 0 .48-.11.69-.31.2-.19.3-.42.31-.69H8V7.98v.01zM7 2.3c-3.14 0-5.7 2.54-5.7 5.68 0 3.14 2.56 5.7 5.7 5.7s5.7-2.55 5.7-5.7c0-3.15-2.56-5.69-5.7-5.69v.01zM7 .98c3.86 0 7 3.14 7 7s-3.14 7-7 7-7-3.12-7-7 3.14-7 7-7z"></path></svg></span>note</div><div class="admonitionContent_S0QG"><p>For creating MLModels and MLGroups, please refer to <a href="/docs/api/tutorials/ml">AI/ML Integration Guide</a>.</p></div></div><h3 class="anchor anchorWithStickyNavbar_LWe7" id="create-mlfeature">Create MlFeature<a href="#create-mlfeature" class="hash-link" aria-label="Direct link to Create MlFeature" title="Direct link to Create MlFeature"></a></h3><p>An ML Feature represents an instance of a feature that can be used across different machine learning models. Features are organized into Feature Tables to be consumed by machine learning models. For example, if we were modeling features for a Users Feature Table, the Features would be <code>age</code>, <code>sign_up_date</code>, <code>active_in_past_30_days</code> and so forth.Using Features in DataHub allows users to see the sources a feature was generated from and how a feature is used to train models.</p><div class="tabs-container tabList__CuJ"><ul role="tablist" aria-orientation="horizontal" class="tabs"><li role="tab" tabindex="0" aria-selected="true" class="tabs__item tabItem_LNqP tabs__item--active">Python</li></ul><div class="margin-top--md"><div role="tabpanel" class="tabItem_Ymn6"><div class="language-python codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#bfc7d5;--prism-background-color:#292d3e"><div class="codeBlockContent_biex"><pre tabindex="0" class="prism-code language-python codeBlock_bY9V thin-scrollbar"><code class="codeBlockLines_e6Vv"><span class="token-line" style="color:#bfc7d5"><span class="token comment" style="color:rgb(105, 112, 152);font-style:italic"># Inlined from /metadata-ingestion/examples/library/create_mlfeature.py</span><span class="token plain"></span><br></span><span class="token-line" style="color:#bfc7d5"><span class="token plain"></span><span class="token keyword" style="font-style:italic">import</span><span class="token plain"> datahub</span><span class="token punctuation" style="color:rgb(199, 146, 234)">.</span><span class="token plain">emitter</span><span class="token punctuation" style="color:rgb(199, 146, 234)">.</span><span class="token plain">mce_builder </span><span class="token keyword" style="font-style:italic">as</span><span class="token plain"> builder</span><br></span><span class="token-line" style="color:#bfc7d5"><span class="token plain"></span><span class="token keyword" style="font-style:italic">import</span><span class="token plain"> datahub</span><span class="token punctuation" style="color:rgb(199, 146, 234)">.</span><span class="token plain">metadata</span><span class="token punctuation" style="color:rgb(199, 146, 234)">.</span><span class="token plain">schema_classes </span><span class="token keyword" style="font-style:italic">as</span><span class="token plain"> models</span><br></span><span class="token-line" style="color:#bfc7d5"><span class="token plain"></span><span class="token keyword" style="font-style:italic">from</span><span class="token plain"> datahub</span><span class=
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