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<title data-rh="true">AI/ML Framework Integration with DataHub | DataHub</title><meta data-rh="true" name="viewport" content="width=device-width,initial-scale=1"><meta data-rh="true" name="twitter:card" content="summary_large_image"><meta data-rh="true" property="og:url" content="https://docs.datahub.com/docs/api/tutorials/ml"><meta data-rh="true" name="docusaurus_locale" content="en"><meta data-rh="true" name="docsearch:language" content="en"><meta data-rh="true" name="docusaurus_version" content="current"><meta data-rh="true" name="docusaurus_tag" content="docs-default-current"><meta data-rh="true" name="docsearch:version" content="current"><meta data-rh="true" name="docsearch:docusaurus_tag" content="docs-default-current"><meta data-rh="true" property="og:title" content="AI/ML Framework Integration with DataHub | DataHub"><meta data-rh="true" name="description" content="Why Integrate Your AI/ML System with DataHub?"><meta data-rh="true" property="og:description" content="Why Integrate Your AI/ML System with DataHub?"><link data-rh="true" rel="icon" href="/img/favicon.ico"><link data-rh="true" rel="canonical" href="https://docs.datahub.com/docs/api/tutorials/ml"><link data-rh="true" rel="alternate" href="https://docs.datahub.com/docs/api/tutorials/ml" hreflang="en"><link data-rh="true" rel="alternate" href="https://docs.datahub.com/docs/api/tutorials/ml" hreflang="x-default"><link data-rh="true" rel="preconnect" href="https://RK0UG797F3-dsn.algolia.net" crossorigin="anonymous"><link rel="alternate" type="application/rss+xml" href="/learn/rss.xml" title="DataHub RSS Feed">
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.cloud-cta {
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font-weight: 600;
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background: linear-gradient(40deg, var(--ifm-menu-color-active), var(--ifm-menu-color-active));
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background-clip: text;
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transition: background-image 0.3s ease;
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}
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.cloud-cta:hover {
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color: transparent;
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background: linear-gradient(40deg, var(--ifm-menu-color-active), #ff1493);
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background-size: 200% 100%;
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-webkit-background-clip: text;
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background-clip: text;
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animation: gradientShift 3s ease infinite;
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}
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@keyframes gradientShift {
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0%, 100% { background-position: 0% 50%; }
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50% { background-position: 100% 50%; }
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}
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<div class="cloud-cta">Get Cloud</div>
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<img class="slack-logo" src="https://upload.wikimedia.org/wikipedia/commons/d/d5/Slack_icon_2019.svg" , alt="slack" , height="20px" style="margin: 10px 0 0 0;">
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DataHub makes this easier by providing a central place to organize and track your AI assets.</p><p>This guide will show you how to integrate your AI workflows with DataHub.
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With integrations for popular ML platforms like MLflow and Amazon SageMaker, DataHub enables you to easily find and share AI models across your organization, track how models evolve over time, and understand how training data connects to each model.
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Most importantly, it enables seamless collaboration on AI projects by making everything discoverable and connected.</p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="goals-of-this-guide">Goals Of This Guide<a href="#goals-of-this-guide" class="hash-link" aria-label="Direct link to Goals Of This Guide" title="Direct link to Goals Of This Guide"></a></h2><p>In this guide, you'll learn how to:</p><ul><li>Create your basic AI components (models, experiments, runs)</li><li>Connect these components to build a complete AI system</li><li>Track relationships between models, data, and experiments</li></ul><h2 class="anchor anchorWithStickyNavbar_LWe7" id="core-ai-concepts">Core AI Concepts<a href="#core-ai-concepts" class="hash-link" aria-label="Direct link to Core AI Concepts" title="Direct link to Core AI Concepts"></a></h2><p>Here's what you need to know about the key components in DataHub:</p><ul><li><strong>Experiments</strong> are collections of training runs for the same project, like all attempts to build a churn predictor</li><li><strong>Training Runs</strong> are attempts to train a model within an experiment, capturing parameters and results</li><li><strong>Model Groups</strong> organize related models together, like all versions of your churn predictor</li><li><strong>Models</strong> are successful training runs registered for production use</li></ul><p align="center"><img loading="lazy" width="70%" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/apis/tutorials/ml/concept-diagram-dh-term.png" class="img_ev3q"></p><p>The hierarchy works like this:</p><ol><li>Every run belongs to an experiment</li><li>Successful runs can be registered as models</li><li>Models belong to a model group</li><li>Not every run becomes a model</li></ol><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>Terminology Mapping</div><div class="admonitionContent_S0QG"><p>Different AI platforms (MLflow, Amazon SageMaker) have their own terminology.
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To keep things consistent, we'll use DataHub's terms throughout this guide.
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Here's how DataHub's terminology maps to these platforms:</p><table><thead><tr><th>DataHub</th><th>Description</th><th>MLflow</th><th>SageMaker</th></tr></thead><tbody><tr><td>ML Model Group</td><td>Collection of related models</td><td>Model</td><td>Model Group</td></tr><tr><td>ML Model</td><td>Versioned artifact in a model group</td><td>Model Version</td><td>Model Version</td></tr><tr><td>ML Training Run</td><td>Single training attempt</td><td>Run</td><td>Training Job</td></tr><tr><td>ML Experiment</td><td>Collection of training runs</td><td>Experiment</td><td>Experiment</td></tr></tbody></table></div></div><p>For platform-specific details, see our integration guides for <a href="/docs/generated/ingestion/sources/mlflow">MLflow</a> and <a href="/docs/generated/ingestion/sources/sagemaker">Amazon SageMaker</a>.</p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="basic-setup">Basic Setup<a href="#basic-setup" class="hash-link" aria-label="Direct link to Basic Setup" title="Direct link to Basic Setup"></a></h2><p>To follow this tutorial, you'll need DataHub Quickstart deployed locally.
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For detailed steps, see the <a href="/docs/quickstart">Datahub Quickstart Guide</a>.</p><p>Next, set up the Python client for DataHub using <code>DatahubAIClient</code>defined in <a href="https://github.com/datahub-project/datahub/blob/master/metadata-ingestion/examples/ai/dh_ai_client.py" target="_blank" rel="noopener noreferrer">here</a>.</p><p>Create a token in DataHub UI and replace <code><your_token></code> with your token:</p><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 keyword" style="font-style:italic">from</span><span class="token plain"> dh_ai_client </span><span class="token keyword" style="font-style:italic">import</span><span class="token plain"> DatahubAIClient</span><br></span><span class="token-line" style="color:#bfc7d5"><span class="token plain" style="display:inline-block"></span><br></span><span class="token-line" style="color:#bfc7d5"><span class="token plain">client </span><span class="token operator" style="color:rgb(137, 221, 255)">=</span><span class="token plain"> DatahubAIClient</span><span class="token punctuation" style="color:rgb(199, 146, 234)">(</span><span class="token plain">token</span><span class="token operator" style="color:rgb(137, 221, 255)">=</span><span class="token string" style="color:rgb(195, 232, 141)">"<your_token>"</span><span class="token punctuation" style="color:rgb(199, 146, 234)">,</span><span class="token plain"> server_url</span><span class="token operator" style="color:rgb(137, 221, 255)">=</span><span class="token string" style="color:rgb(195, 232, 141)">"http://localhost:9002"</span><span class="token punctuation" style="color:rgb(199, 146, 234)">)</span><br></span></code></pre><div class="buttonGroup__atx"><button type="button" aria-label="Copy code to clipboard" title="Copy" class="clean-btn"><span class="copyButtonIcons_eSgA" aria-hidden="true"><svg viewBox="0 0 24 24" class="copyButtonIcon_y97N"><path fill="currentColor" d="M19,21H8V7H19M19,5H8A2,2 0 0,0 6,7V21A2,2 0 0,0 8,23H19A2,2 0 0,0 21,21V7A2,2 0 0,0 19,5M16,1H4A2,2 0 0,0 2,3V17H4V3H16V1Z"></path></svg><svg viewBox="0 0 24 24" class="copyButtonSuccessIcon_LjdS"><path fill="currentColor" d="M21,7L9,19L3.5,13.5L4.91,12.09L9,16.17L19.59,5.59L21,7Z"></path></svg></span></button></div></div></div><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>Verifying via GraphQL</div><div class="admonitionContent_S0QG"><p>Throughout this guide, we'll show how to verify changes using GraphQL queries.
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You can run these queries in the DataHub UI at <code>https://localhost:9002/api/graphiql</code>.</p></div></div><h2 class="anchor anchorWithStickyNavbar_LWe7" id="create-ai-assets">Create AI Assets<a href="#create-ai-assets" class="hash-link" aria-label="Direct link to Create AI Assets" title="Direct link to Create AI Assets"></a></h2><p>Let's create the basic building blocks of your ML system. These components will help you organize your AI work and make it discoverable by your team.</p><h3 class="anchor anchorWithStickyNavbar_LWe7" id="create-a-model-group">Create a Model Group<a href="#create-a-model-group" class="hash-link" aria-label="Direct link to Create a Model Group" title="Direct link to Create a Model Group"></a></h3><p>A model group contains different versions of a similar model. For example, all versions of your "Customer Churn Predictor" would go in one group.</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">Basic</li><li role="tab" tabindex="-1" aria-selected="false" class="tabs__item tabItem_LNqP">Advanced</li></ul><div class="margin-top--md"><div role="tabpanel" class="tabItem_Ymn6">Create a basic model group with just an identifier:<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 plain">model_group </span><span class="token operator" style="color:rgb(137, 221, 255)">=</span><span class="token plain"> MLModelGroup</span><span class="token punctuation" style="color:rgb(199, 146, 234)">(</span><span class="token plain"></span><br></span><span class="token-line" style="color:#bfc7d5"><span class="token plain"> </span><span class="token builtin" style="color:rgb(130, 170, 255)">id</span><span class="token operator" style="color:rgb(137, 221, 255)">=</span><span class="token string" style="color:rgb(195, 232, 141)">"airline_forecast_models_group"</span><span class="token punctuation" style="color:rgb(199, 146, 234)">,</span><span class="token plain"></span><br></span><span class="token-line" style="color:#bfc7d5"><span class="token plain"> platform</span><span class="token operator" style="color:rgb(137, 221, 255)">=</span><span class="token string" style="color:rgb(195, 232, 141)">"mlflow"</span><span class="token punctuation" style="color:rgb(199, 146, 234)">,</span><span class="token plain"></span><br></span><span class="token-line" style="color:#bfc7d5"><span class="token plain"></span><span class="token punctuation" style="color:rgb(199, 146, 234)">)</span><span class="token plain"></span><br></span><span class="token-line" style="color:#bfc7d5"><span class="token plain" style="display:inline-block"></span><br></span><span class="token-line" style="color:#bfc7d5"><span class="token plain">client</span><span class="token punctuation" style="color:rgb(199, 146, 234)">.</span><span class="token plain">_emit_mcps</span><span class="token punctuation" style="color:rgb(199, 146, 234)">(</span><span class="token plain">model_group</span><span class="token punctuation" style="color:rgb(199, 146, 234)">.</span><span class="token plain">as_mcps</span><span class="token punctuation" style="color:rgb(199, 146, 234)">(</span><span class="token punctuation" style="color:rgb(199, 146, 234)">)</span><span class="token punctuation" style="color:rgb(199, 146, 234)">)</span><br></span></code></pre><div class="buttonGroup__atx"><button type="button" aria-label="Copy code to clipboard" title="Copy" class="clean-btn"><span class="copyButtonIcons_eSgA" aria-hidden="true"><svg viewBox="0 0 24 24" class="copyButtonIcon_y97N"><path fill="currentColor" d="M19,21H8V7H19M19,5H8A2,2 0 0,0 6,7V21A2,2 0 0,0 8,23H19A2,2 0 0,0 21,21V7A2,2 0
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