Cloud Functions for Real-time AI Model Evaluation on GCP

Question

Pulumi · Accepted Answer

Building Cloud Functions for real-time AI model evaluation on Google Cloud Platform (GCP) can be a powerful way to deploy machine learning models that can instantly process incoming requests and return predictions. In this setup, a function is triggered in real-time, perhaps by an HTTP request or by new data appearing in a GCP resource like a Pub/Sub topic or a Cloud Storage bucket. The function then uses a pre-trained AI model to evaluate the input data and returns a result.

To achieve this, we'll use two main components from the GCP product suite:

1. **Cloud Functions**: This is a serverless execution environment where you can write simple, single-purpose functions that respond to cloud events without the need to manage a server or runtime environment.

2. **AI Platform Predictions**: A part of the AI Platform on GCP that allows deployment of machine learning models to GCP infrastructure. The models can then be used to predict outcomes on new data.

Here's what we will do:

- Define a Cloud Function using Pulumi that will trigger on an HTTP request.
- Deploy an AI model to AI Platform Predictions.
- Within the Cloud Function, call the AI Platform Predictions service to evaluate the data that triggered the function.

Below is a basic Pulumi program written in Python that sets up a Cloud Function to trigger on an HTTP request. This function will be set up to call a hypothetical AI model that we assume has been uploaded to AI Platform. Please note that we aren't actually deploying an AI model in this program; we're assuming it's already been deployed and is available via AI Platform Predictions.

Let's walk through the code step by step.

```python
import pulumi
import pulumi_gcp as gcp

# Define the GCP project and location (region) we'll be working in
project = 'my-project-id'
region = 'us-central1'

# Deploy the AI model to AI Platform Predictions
# Note: This step assumes that you have already trained and uploaded your model to GCP.
# You should replace `model_name` and `model_version` with your actual model details.
model_name = 'my_model'
model_version = 'v1'

# Define the Cloud Function
# Here we define a Cloud Function that will trigger on HTTP requests and make predictions using the deployed AI model.
function = gcp.cloudfunctions.Function('function',
    entry_point='predict',  # Name of the function in your code
    runtime='python37',  # The runtime environment for the Cloud Function
    trigger_http=True,  # This means the function will be triggered by HTTP requests
    available_memory_mb=128,  # Assign 128 MB of memory to the function
    timeouts='60s',  # The function will timeout if it runs longer than 60 seconds
    project=project,
    region=region,
    environment_variables={
        'MODEL_NAME': model_name,
        'MODEL_VERSION': model_version,
    }  # Pass the model information to the function via environment variables
)

# Export the HTTP url of the function as a stack output
pulumi.export('function_url', function.https_trigger_url)

# Note: Your actual function code ('predict') will need to be deployed to the Cloud Function,
# which can be done using the GCP console or the gcloud CLI tool alongside Pulumi.
# The 'predict' function will need to handle calling the AI Platform Predictions API with the received data.
```

In this program, we define a Cloud Function that is triggered via HTTP. When the function is invoked, it processes the incoming request to perform model evaluation. The function uses the environment variables `MODEL_NAME` and `MODEL_VERSION` to know which model to run for predictions. We set these variables based on the model you've previously deployed to AI Platform Predictions.

To finish this setup, you would also need to deploy the actual code for the `predict` function, which involves making the call to the AI Platform Predictions API. This part is done outside of Pulumi, and typically involves writing the function code in a file, packaging it according to GCP's requirements, and deploying it using the `gcloud` command-line tool or directly through the GCP console.

Once deployed, Pulumi provides you with the function's HTTP trigger URL. You can use this URL to send requests to your Cloud Function.

Remember, before running this Pulumi program, you would need:

- Pulumi CLI installed and set up with access to your GCP account.
- A GCP Project with billing enabled.
- The AI model already deployed to AI Platform Predictions.

This Pulumi program provides a starting point, and likely you'll need to adjust and expand upon it to fit the specifics of your use case and ensure proper security, error handling, and scaling based on the volume of your requests.