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In the Google Cloud console, on the project selector page,
select or create a Google Cloud project.
[[["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)：2025-08-11。"],[],[],null,["# Quickstart: Build an app in the console\n\nBuild an app in the console\n===========================\n\nLearn how to create a simple Vertex AI Vision object detector app in the\nGoogle Cloud console.\n\n*** ** * ** ***\n\nTo follow step-by-step guidance for this task directly in the\nGoogle Cloud console, click **Guide me**:\n\n[Guide me](https://console.cloud.google.com/freetrial?redirectPath=/?walkthrough_id=vertex-ai-vision--build-app-console-quickstart)\n\n*** ** * ** ***\n\nBefore you begin\n----------------\n\n- Sign in to your Google Cloud account. If you're new to Google Cloud, [create an account](https://console.cloud.google.com/freetrial) to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.\n- In the Google Cloud console, on the project selector page,\n select or create a Google Cloud project.\n\n | **Note**: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n-\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n-\n\n\n Enable the Vision AI API.\n\n\n [Enable the API](https://console.cloud.google.com/flows/enableapi?apiid=visionai.googleapis.com)\n\n- In the Google Cloud console, on the project selector page,\n select or create a Google Cloud project.\n\n | **Note**: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n-\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n-\n\n\n Enable the Vision AI API.\n\n\n [Enable the API](https://console.cloud.google.com/flows/enableapi?apiid=visionai.googleapis.com)\n\nCreate an object detector application\n-------------------------------------\n\nAfter you have set up your environment, you can create your app.\n\nIn the Google Cloud console, an app is represented as a graph.\nAdditionally, in Vertex AI Vision, an app graph must have at least two nodes: a\nvideo source node (stream), and *at least* one more node (a processing model or\noutput destination).\n\n### Create an empty app\n\nBefore you can populate the app graph, you must first create an empty app. \n\n### Console\n\nCreate an app in the Google Cloud console.\n\n1. Open the **Applications** tab of the Vertex AI Vision dashboard.\n\n [Go to the Applications tab](https://console.cloud.google.com/ai/vision-ai/applications)\n2. Click the add**Create** button.\n\n3. Enter `quickstart-app` as the app name and choose your region.\n\n4. Click **Create**.\n\n \u003cbr /\u003e\n\n### Add app component nodes\n\nAfter you have created the empty application, you can then add the three nodes\nto the app graph: the **ingestion node** that can receive stream data, the\n**processing node** that performs a computer image task on data, and a **data\ndestination node**, a warehouse storage destination in this example. \n\n### Console\n\nAdd component nodes to your app in the console.\n\n1. Open the **Applications** tab of the Vertex AI Vision dashboard.\n\n [Go to the Applications tab](https://console.cloud.google.com/ai/vision-ai/applications)\n2. In the `quickstart-app` line, select\n schema**View graph**. This takes you\n to the graph visualization of the processing pipeline.\n\n**Add a data ingestion node**\n\n1. To add an input stream node, select the **Streams** option in the\n **Connectors** section of the side menu.\n\n2. In the **Source** section of the **Stream** menu that opens, select\n add**Add streams**.\n\n3. In the **Add streams** menu, choose\n radio_button_checked**Register new\n streams** and add `quickstart-stream` as the stream name.\n\n \u003cbr /\u003e\n\n4. To add the stream to the app graph, click **Add streams**.\n\n**Add a data processing node**\n\n1. To add the object detector model node, select the **Object detector**\n option in the **Pre-trained models** section of the side menu.\n\n**Add a data storage node**\n\n1. To add the output destination (storage) node, select the\n **Vertex AI Vision's Media Warehouse** option in the **Connectors** section of the side\n menu.\n\n2. In the **Vertex AI Vision's Media Warehouse** menu, click **Connect warehouse**.\n\n3. In the **Connect warehouse** menu, select\n radio_button_checked**Create new\n warehouse** . Name the warehouse `quickstart-warehouse`, and leave\n the TTL duration at 14 days.\n\n4. Click the **Create** button to add the warehouse.\n\nDeploy your app for use\n-----------------------\n\nAfter you have built your end-to-end app with all the necessary components, the last step to using the app is to deploy it.\n\n\u003cbr /\u003e\n\n### Console\n\n1. Open the **Applications** tab of the Vertex AI Vision dashboard.\n\n [Go to the Applications tab](https://console.cloud.google.com/ai/vision-ai/applications)\n2. Select **View graph** next to the `quickstart-app` app in the list.\n\n3. From the application graph builder page, click the\n play_arrow**Deploy** button.\n\n4. In the following confirmation dialog, select **Deploy**.\n\n The deploy operation might take several minutes to complete. After\n deployment finishes, green check marks appear next to the nodes.\n\n\nCongratulations! You've just created and deployed your first Vertex AI Vision\napp. Creating and deploying an app are the first steps in ingesting and using\nprocessed media data with Vertex AI Vision.\n\nClean up\n--------\n\nTo avoid incurring charges to your Google Cloud account for the resources used\nin this quickstart, either delete the project that contains the resources, or\nkeep the project and delete the individual resources. \n\n### Delete the project\n\n| **Caution** : Deleting a project has the following effects:\n|\n| - **Everything in the project is deleted.** If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.\n| - **Custom project IDs are lost.** When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an `appspot.com` URL, delete selected resources inside the project instead of deleting the whole project.\n|\n|\n| If you plan to explore multiple architectures, tutorials, or quickstarts, reusing projects\n| can help you avoid exceeding project quota limits.\n1. In the Google Cloud console, go to the **Manage resources** page.\n\n [Go to Manage resources](https://console.cloud.google.com/iam-admin/projects)\n2. In the project list, select the project that you want to delete, and then click **Delete**.\n3. In the dialog, type the project ID, and then click **Shut down** to delete the project. \n\n### Delete individual resources\n\n#### Delete a warehouse\n\n1. In the Google Cloud console, go to the **Warehouses** page.\n\n [Go to the Warehouses tab](https://console.cloud.google.com/ai/vision-ai/media-warehouse)\n2. Locate your `quickstart-warehouse` warehouse.\n3. To delete the warehouse, click more_vert **Actions** , click **Delete warehouse**, and then follow the instructions.\n\n#### Delete a stream\n\n1. In the Google Cloud console, go to the **Streams** page.\n\n [Go to the Streams tab](https://console.cloud.google.com/ai/vision-ai/video-streams)\n2. Locate your `quickstart-stream` stream.\n3. To delete the stream, click more_vert **Actions** , click **Delete stream**, and then follow the instructions.\n\n#### Delete an app\n\n1. In the Google Cloud console, go to the **Applications** page.\n\n [Go to the Applications tab](https://console.cloud.google.com/ai/vision-ai/applications)\n | **Note:** You must first undeploy your app before you can delete it.\n2. Locate your `quickstart-app` app.\n3. To delete the app, click more_vert **Actions** , click **Delete application**, and then follow the instructions.\n\nWhat's next\n-----------\n\n- Read [Set up a project and a development environment](/vision-ai/docs/cloud-environment) before you use the command line tools.\n- Learn how to [ingest data](/vision-ai/docs/create-manage-streams#ingest-videos) into your new app and read about other components you can add in [Build an app](/vision-ai/docs/build-app).\n- Learn about other output storage and processing options in [Connect app output to a data destination](/vision-ai/docs/connect-data-destination).\n- Read about how to [Search Warehouse data in the console](/vision-ai/docs/search-streaming-warehouse).\n- Read more about [Responsible AI practices](https://ai.google/responsibilities/responsible-ai-practices/)."]]
