Google Cloud Storage and Google Vertex AI Integration

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Automate training data management: store datasets in Google Cloud Storage, then use Google Vertex AI for model training, enhanced by Latenode’s affordable execution pricing. Customize the logic further with JavaScript for advanced data transformations.

Swap Apps

Google Cloud Storage

Google Vertex AI

Step 1: Choose a Trigger

Step 2: Choose an Action

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How to connect Google Cloud Storage and Google Vertex AI

Create a New Scenario to Connect Google Cloud Storage and Google Vertex AI

In the workspace, click the “Create New Scenario” button.

Add the First Step

Add the first node – a trigger that will initiate the scenario when it receives the required event. Triggers can be scheduled, called by a Google Cloud Storage, triggered by another scenario, or executed manually (for testing purposes). In most cases, Google Cloud Storage or Google Vertex AI will be your first step. To do this, click "Choose an app," find Google Cloud Storage or Google Vertex AI, and select the appropriate trigger to start the scenario.

Add the Google Cloud Storage Node

Select the Google Cloud Storage node from the app selection panel on the right.

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Configure the Google Cloud Storage

Click on the Google Cloud Storage node to configure it. You can modify the Google Cloud Storage URL and choose between DEV and PROD versions. You can also copy it for use in further automations.

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Add the Google Vertex AI Node

Next, click the plus (+) icon on the Google Cloud Storage node, select Google Vertex AI from the list of available apps, and choose the action you need from the list of nodes within Google Vertex AI.

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Authenticate Google Vertex AI

Now, click the Google Vertex AI node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Google Vertex AI settings. Authentication allows you to use Google Vertex AI through Latenode.

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Configure the Google Cloud Storage and Google Vertex AI Nodes

Next, configure the nodes by filling in the required parameters according to your logic. Fields marked with a red asterisk (*) are mandatory.

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Set Up the Google Cloud Storage and Google Vertex AI Integration

Use various Latenode nodes to transform data and enhance your integration:

  • Branching: Create multiple branches within the scenario to handle complex logic.
  • Merging: Combine different node branches into one, passing data through it.
  • Plug n Play Nodes: Use nodes that don’t require account credentials.
  • Ask AI: Use the GPT-powered option to add AI capabilities to any node.
  • Wait: Set waiting times, either for intervals or until specific dates.
  • Sub-scenarios (Nodules): Create sub-scenarios that are encapsulated in a single node.
  • Iteration: Process arrays of data when needed.
  • Code: Write custom code or ask our AI assistant to do it for you.
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Save and Activate the Scenario

After configuring Google Cloud Storage, Google Vertex AI, and any additional nodes, don’t forget to save the scenario and click "Deploy." Activating the scenario ensures it will run automatically whenever the trigger node receives input or a condition is met. By default, all newly created scenarios are deactivated.

Test the Scenario

Run the scenario by clicking “Run once” and triggering an event to check if the Google Cloud Storage and Google Vertex AI integration works as expected. Depending on your setup, data should flow between Google Cloud Storage and Google Vertex AI (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.

Most powerful ways to connect Google Cloud Storage and Google Vertex AI

Google Cloud Storage + Google Vertex AI + Google Sheets: When a new file is uploaded to Google Cloud Storage, it is analyzed using Google Vertex AI's Gemini model. The analysis results are then logged in a Google Sheet for review.

Google Cloud Storage + Google Vertex AI + Slack: When a file is uploaded to Google Cloud Storage, it's processed by Google Vertex AI. If the analysis detects anomalies based on criteria in the model, a notification is sent to the data science team via Slack.

Google Cloud Storage and Google Vertex AI integration alternatives

About Google Cloud Storage

Use Google Cloud Storage in Latenode for automated file management. Upload, download, and manage files in your workflows. Automate backups, data archiving, or image processing. Connect GCS to other apps for seamless data transfer and triggering events. Latenode's visual editor simplifies complex file-based automations.

About Google Vertex AI

Use Vertex AI in Latenode to build AI-powered automation. Quickly integrate machine learning models for tasks like sentiment analysis or image recognition. Automate data enrichment or content moderation workflows without complex coding. Latenode’s visual editor makes it easier to chain AI tasks and scale them reliably, paying only for the execution time of each flow.

See how Latenode works

FAQ Google Cloud Storage and Google Vertex AI

How can I connect my Google Cloud Storage account to Google Vertex AI using Latenode?

To connect your Google Cloud Storage account to Google Vertex AI on Latenode, follow these steps:

  • Sign in to your Latenode account.
  • Navigate to the integrations section.
  • Select Google Cloud Storage and click on "Connect".
  • Authenticate your Google Cloud Storage and Google Vertex AI accounts by providing the necessary permissions.
  • Once connected, you can create workflows using both apps.

Can I analyze images stored in Cloud Storage using Vertex AI?

Yes, you can! Latenode simplifies this integration, allowing you to trigger Vertex AI analysis whenever new images are added to Cloud Storage, automating image processing workflows.

What types of tasks can I perform by integrating Google Cloud Storage with Google Vertex AI?

Integrating Google Cloud Storage with Google Vertex AI allows you to perform various tasks, including:

  • Automatically tagging images stored in Cloud Storage using Vertex AI's vision models.
  • Generating text summaries of documents stored in Cloud Storage using Vertex AI's language models.
  • Classifying files in Cloud Storage based on content identified by Vertex AI.
  • Detecting objects in videos stored in Cloud Storage.
  • Training custom AI models using data stored in Google Cloud Storage.

Can I monitor changes in my Cloud Storage bucket using Latenode?

Yes, Latenode allows you to monitor your Cloud Storage buckets and trigger automated workflows when new files are added or modified.

Are there any limitations to the Google Cloud Storage and Google Vertex AI integration on Latenode?

While the integration is powerful, there are certain limitations to be aware of:

  • Large file transfers may be subject to Google Cloud Storage API limits.
  • The number of concurrent Vertex AI requests may be limited by your Google Cloud project quota.
  • Complex data transformations may require custom JavaScript code within Latenode.

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