Custom Anomaly Detection Rules for AI Applications

Question

Pulumi · Accepted Answer

Creating custom anomaly detection rules for AI applications involves setting up specific metrics and thresholds that, when exceeded, indicate unusual or unexpected behavior. These rules can be a critical part of monitoring AI applications, allowing you to respond proactively to potential issues.

In the context of cloud and infrastructure as code, such rules are often configured within the monitoring tools provided by cloud service providers or third-party services integrated with your cloud infrastructure.

Below, we'll write a Pulumi program in Python that sets up custom anomaly detection rules for an AI application using the OCI (Oracle Cloud Infrastructure) AI Anomaly Detection service. This service enables you to create models that learn from your data and subsequently detect anomalies.

In this example, we'll create a project and a model within OCI's AI Anomaly Detection service. The project acts as a logical container for your models, data, and jobs, and the model is what will learn from your data and perform the actual anomaly detection.

Each resource in the Pulumi program corresponds to an OCI resource that will be provisioned. For the AI Anomaly Detection service, this typically involves provisioning a `Project` and a `Model`. Here's how you might set that up using Pulumi and Python:

```python
import pulumi
import pulumi_oci as oci

# Create an Oracle Cloud Infrastructure AI Anomaly Detection project.
anomaly_detection_project = oci.aianomalydetection.Project("anomalyDetectionProject",
    compartment_id="ocid1.compartment.oc1..exampleuniqueID",  # Replace with your compartment ID
    description="Anomaly detection project for AI application",
    display_name="AIAppAnomalyDetectionProject"
)

# Create a model within the project for anomaly detection.
anomaly_detection_model = oci.aianomalydetection.Model("anomalyDetectionModel",
    project_id=anomaly_detection_project.id,  # Reference the project ID from the project resource
    compartment_id="ocid1.compartment.oc1..exampleuniqueID",  # Replace with your compartment ID
    display_name="AIAnomalyDetectionModel",
    description="Model to detect anomalies in AI application",
    model_training_details=oci.aianomalydetection.ModelModelTrainingDetailsArgs(
        # Set the data and parameters for model training here
        # For example, target F1 score, which balances precision and recall
        target_fap=0.7,
        # Model window size for analysis (number of data points)
        window_size=50,
        data_asset_ids=["ocid1.dataasset.oc1..exampleuniqueID"],  # Replace with your data asset IDs
    )
    # You can add more configuration details as needed for your training setup and data
)

# Export the project and model IDs so you can easily locate them in the OCI console or use them in other parts of your infrastructure.
pulumi.export("anomaly_detection_project_id", anomaly_detection_project.id)
pulumi.export("anomaly_detection_model_id", anomaly_detection_model.id)
```

In this program:
- We import the necessary Pulumi and OCI packages.
- We create an anomaly detection `Project`, which acts as a container for the AI model and associated resources. Replace the `compartment_id` with the ID of your OCI compartment.
- Next, we provision an anomaly detection `Model` that specifies how the system should learn from the data to detect anomalies. The `project_id` is sourced from the project we created above, ensuring that the model is associated with the correct project. Training details such as `target_fap`, `window_size`, and `data_asset_ids` are defined to set up the model training criteria. These parameters should be adjusted to your specific use case.
- Finally, we export the generated IDs for the project and model as stack outputs so that they can be easily found and referenced.

To run this Pulumi program, save it to a file (e.g., `anomaly_detection.py`), and execute it using the Pulumi CLI by running `pulumi up`. This will provision the defined resources in your OCI account.

Adjust the example code to match your specific AI application's monitoring needs, including the data sources, training details, and any additional configuration that OCI's AI Anomaly Detection service allows you to specify.