Exercise – leveraging pre-built GCP models as a service 

In this exercise, we want to use a GCP service called Google Cloud Vision. Google Cloud Vision is one of many pre-built models in GCP. In pre-built models, we only need to call the API from our application. This means that we don't need to create an ML model. 

In this exercise, we will create a Python application that can read an image with handwritten text and convert it into a Python string. 

The following are the steps for this exercise:

1. Upload the image to a GCS bucket.
2. Install the required Python packages.
3. Create a detect text function in Python.

Let's start by uploading the image.

Uploading the image to a GCS bucket

In the GCS console, go to the bucket that you created in the previous chapters. For example, my bucket is packt-data-eng-on-gcp-data-bucket.

Inside the bucket, create a new folder called chapter-8. This is an example from my console:

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