To implement a similar solution, follow the steps below.
Steps 1 – 2: Upload image and video data to Cloud Storage
Upload all image and video files to a Cloud Storage bucket. For the demo, we’ve downloaded some images and videos from Google Search that are available on GitHub. Be sure to remove the README.md file before uploading them to your Cloud Storage bucket.
Prepare your media files:
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Using your own data, collect all the images and video files you plan to work with.
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Ensure the files are organized and named appropriately for easy management and access.
Upload data to Cloud Storage:
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Create a Cloud Storage bucket, if you haven’t already.
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Upload your media files into the bucket. You can use the Google Cloud console, the
gsutilcommand-line tool, or the Cloud Storage API. -
Verify that the files are uploaded correctly and note the bucket’s name and path where the files are stored (e.g.,
gs://your-bucket-name/your-files).
Step 3: Create an object table in BigQuery
Create an Object table in BigQuery to point to your source image and video files in the Cloud Storage bucket. Object tables are read-only tables over unstructured data objects that reside in Cloud Storage. You can learn about other use cases for BigQuery object tables here.
Before you create the object table, establish a connection, as described here. Ensure that the connection’s principal has the ‘Vertex AI User’ role and that the Vertex AI API is enabled for your project.
Create remote connection
