[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC):2025-08-19。"],[],[],null,["# Track the lineage of pipeline artifacts\n\nEach pipeline run created using Vertex AI Pipelines has several associated\nartifacts and parameters, such as models, datasets, pipeline templates, and\ncomponents. The lineage of a pipeline artifact includes the factors that\ncontributed to its creation, as well as artifacts and metadata derived from\nthe artifact. For example, a model's lineage can include the following:\n\n- The training, test, and evaluation data used to create the model.\n\n- The hyperparameters used during model training.\n\n- Metadata recorded from the training and evaluation process, such as the model's accuracy.\n\n- Artifacts that descend from this model, such as the results of batch predictions.\n\nYou can use this metadata to help answer questions like the following:\n\n- Why did a certain pipeline run produce an especially accurate model?\n\n- Which pipeline run produced the most accurate model, and what hyperparameters were used to train the model?\n\n- Depending on the steps in your pipeline, you might be able to answer system\n governance questions. For example, you could use metadata to determine which\n version of your model was in production at a given point in time.\n\nTo view and analyze the pipeline artifact lineage, you can use either Vertex ML Metadata or Dataplex Universal Catalog.\n\nThe following table outlines the differences between Vertex ML Metadata and Dataplex Universal Catalog:\n\nMap Vertex ML Metadata artifacts to Dataplex Universal Catalog\n--------------------------------------------------------------\n\nTo map Vertex ML Metadata artifacts to FQNs in Dataplex Universal Catalog,\nyou need to do the following:\n\n- Use Google Cloud Pipeline Components while creating Vertex AI models and\n managed datasets.\n\n- Use custom schema titles (`google.VertexDataset` or `google.VertexModel`)\n while specifying the model or managed dataset resource name in the `metadata`\n field, as illustrated in the following sample:\n\n {\n \"name\": \"projects/example-project/locations/us-central1/metadataStores/default/artifacts/example-artifact\",\n \"displayName\": \"My dataset\",\n \"uri\": \"https://us-central1-aiplatform.googleapis.com/v1/projects/example-project/locations/us-central1/datasets/example-dataset\",\n ...\n \"schemaTitle\": \"google.VertexDataset\",\n \"schemaVersion\": \"0.0.1\",\n \"metadata\": {\n \"resourceName\": \"projects/example-project/locations/us-central1/datasets/example-dataset\"\n }\n }\n\nAnalyze the lineage of pipeline artifacts using Vertex ML Metadata\n------------------------------------------------------------------\n\nWhen you run a pipeline using Vertex AI Pipelines, the artifacts and\nparameters of your pipeline run are stored using Vertex ML Metadata.\nVertex ML Metadata makes it easier to analyze the *lineage* of your\npipeline's artifacts, by saving you the difficulty of keeping track of your\npipeline's metadata.\n\nIf you're new to Vertex ML Metadata, read the [introduction to\nVertex ML Metadata](/vertex-ai/docs/ml-metadata/introduction).\n| For a step-by-step tutorial on analyzing artifacts and metadata generated across your Vertex AI Pipelines executions, see the [Using Vertex ML Metadata with Vertex AI Pipelines](https://codelabs.developers.google.com/vertex-mlmd-pipelines#0) codelab.\n\nFollow these instructions to view the lineage graph for a pipeline\nartifact using Vertex ML Metadata:\n\n1. In the Google Cloud console, in the Vertex AI section, go\n to the **Metadata** page.\n\n [Go to Metadata](https://console.cloud.google.com/vertex-ai/metadata)\n\n The Metadata page lists the artifacts that have been created in the\n **default** metadata store.\n2. In the **Region** drop-down list, select the region that your run was\n created in.\n\n3. Click the **Display name** of an artifact to see its lineage graph.\n\n A static graph showing the artifacts and executions that are a part of this\n lineage graph appears.\n4. Click an artifact or execution to learn more about it.\n\nAnalyze the lineage of pipeline artifacts using Dataplex Universal Catalog\n--------------------------------------------------------------------------\n\nDataplex Universal Catalog discovers metadata from\nGoogle Cloud resources, which include Vertex AI Pipelines\nartifacts like Vertex AI models, managed datasets, and other\nGoogle Cloud resources discoverable in Dataplex Universal Catalog. You can\ndiscover these artifacts using the metadata search capability of\nDataplex Universal Catalog and view their lineage graphs.\n\nFor more information about the Dataplex Universal Catalog metadata search capability,\nsee [Search for resources in Dataplex Universal Catalog](/dataplex/docs/search-assets).\n\nNote that Dataplex Universal Catalog might not be available in all regions where\nVertex AI Pipelines is supported. If Dataplex Universal Catalog is\nunsupported in your region, use Vertex ML Metadata.\n[View the list of supported regions for Dataplex Universal Catalog.](/dataplex/docs/locations)\n\nFollow these instructions to view the lineage graph for a pipeline artifact\non Dataplex Universal Catalog:\n\n1. To launch a Dataplex Universal Catalog search query in the Google Cloud console,\n go to the Dataplex Universal Catalog **Search** page.\n\n [Go to Search](https://console.cloud.google.com/dataplex/dp-search)\n2. Select **Dataplex Universal Catalog** as the search mode.\n\n3. Use the filters to search for the artifacts. For example, you can use the\n **Data types** filter to specify the type of artifact, such as model, dataset,\n or BigQuery table. For more information,\n see [Search for resources in Dataplex Universal Catalog](/dataplex/docs/search-assets).\n\n You can also [define your query in the search field](/dataplex/docs/search-syntax).\n4. To view the lineage of an artifact, click the name of the artifact, and then click the **Lineage** tab.\n\n On the lineage graph, Vertex AI processes are preceded by\n .\n These include pipeline artifacts, pipeline components, and pipeline templates.\n - To view the details of a process, click the process in the lineage graph.\n\n | **Note:** The process details are available only if the process has been catalogued in Dataplex Universal Catalog using a fully qualified name (FQN).\n - For processes based on pipeline tasks from pipeline runs, you can do the following:\n\n - View the pipeline run in Vertex AI by clicking **Open in Vertex AI** in the **Details tab** . To view the runtime details of a pipeline run, such as states, timestamps, and attributes, click **More** . To view the pipeline run in Vertex AI, click **Open in Vertex AI**.\n - For processes based on a pipeline template, you can do the following:\n\n - View the template details in Vertex AI by clicking **Open in Vertex AI** in the **Details tab**.\n\n - View the list of pipeline tasks created in pipeline runs in the\n **Runs** tab. To view the details of the pipeline template in\n Vertex AI, click **More** , and then click\n **Open in Vertex AI**.\n\nWhat's next\n-----------\n\n- Learn how to [run a pipeline](/vertex-ai/docs/pipelines/run-pipeline).\n- Get started [visualizing and analyzing pipeline\n results](/vertex-ai/docs/pipelines/visualize-pipeline).\n- Learn how to [build a machine learning pipeline](/vertex-ai/docs/pipelines/build-pipeline)."]]