Real-time Anomaly Detection Alerts in AI Systems

Question

Pulumi · Accepted Answer

To set up a real-time anomaly detection system within a cloud infrastructure, you can utilize various cloud services that offer AI and machine learning capabilities to analyze data streams and generate alerts when anomalies are detected. Several cloud providers have services specifically for this purpose, for example, AWS offers Lookout for Metrics, and Oracle Cloud Infrastructure (OCI) has AI Anomaly Detection services.

Below is an illustrative example of how you might use Pulumi to provision such a service, specifically using OCI AI Anomaly Detection. In this scenario, we'll create an OCI AI Anomaly Detection project, a model, and a data asset. We'll then configure a detection job to monitor the data for anomalies and set up alerts.

OCI AI Anomaly Detection service allows you to build, train, and deploy machine learning models that can detect anomalies in your data. The `DetectAnomalyJob` resource can then assess datasets in real-time and potentially trigger actions or notifications based on the detection results.

First, let's install the required Pulumi OCI package if it's not already installed:

```sh
pip install pulumi_oci
```

Next, we’ll write a Pulumi program to set up anomaly detection on OCI.

```python
import pulumi
import pulumi_oci as oci

# Initialize the compartment ID where the resources will be created.
# This should be set to your specific OCI compartment identifier.
compartment_id = 'ocid1.compartment.oc1..exampleuniqueID'

# Create an AI Anomaly Detection project
anomaly_detection_project = oci.aianomalydetection.Project("anomalyDetectionProject",
    compartment_id=compartment_id,
    description="Project for real-time anomaly detection",
    display_name="AnomalyDetectionProject",
)

# Define the training details for the anomaly detection model
model_training_details = {
    "targetFap": 0.02,  # False Acceptance Probability
    "windowSize": 12,  # Length of the window used for analysis
    "dataAssetIds": ["/path/to/data/asset"],  # Link to the data asset
    "trainingFraction": 0.7,  # Fraction of dataset to use for training
}

# Creating an AI Anomaly Detection model within the project
anomaly_detection_model = oci.aianomalydetection.Model("anomalyDetectionModel",
    project_id=anomaly_detection_project.id,
    compartment_id=compartment_id,
    description="Model for detecting anomalies",
    display_name="AnomalyDetectionModel",
    model_training_details=model_training_details,
)

# Creating a data asset for the anomaly detection model
data_asset = oci.aianomalydetection.DataAsset("anomalyDataAsset",
    project_id=anomaly_detection_project.id,
    compartment_id=compartment_id,
    display_name="AnomalyDataAsset",
    description="Data asset for the model",
    data_source_details={
        "dataSourceType": "DATA_SOURCE_TYPE",  # Type of the data source (e.g., ORACLE_OBJECT_STORAGE).
        # Fill in additional details specific to the data source type, e.g., bucket details for object storage.
    },
)

# Defining the job to detect anomalies
anomaly_detection_job = oci.aianomalydetection.DetectAnomalyJob("anomalyDetectionJob",
    project_id=anomaly_detection_project.id,
    compartment_id=compartment_id,
    model_id=anomaly_detection_model.id,
    description="Job to detect anomalies",
    display_name="AnomalyDetectionJob",
    input_details={
        "inputType": "INPUT_TYPE", # Specify the type of input source, could be streaming, batch, etc.
        # Additional details based on the input type go here.
    },
    output_details={
        "bucket": "YOUR_BUCKET_NAME",
        "namespace": "YOUR_NAMESPACE",
        "outputType": "OUTPUT_TYPE",  # E.g., Object Storage, etc.
        # Additional output configuration can be set here.
    },
)

# Exposing the ID of the anomaly detection job to use elsewhere in Pulumi or client applications
pulumi.export("anomalyDetectionJobId", anomaly_detection_job.id)
```

This program does the following:
1. Sets up a project under which all anomaly detection resources will be created.
2. Builds an anomaly detection model with specific training details.
3. Creates a data asset that serves as the input for the model.
4. Defines a detect anomaly job to continuously evaluate the data and detect anomalies.

To execute the sample, you must have installed the Pulumi CLI, set up the OCI provider, and configured your OCI credentials.

Remember to adjust the `compartment_id`, `model_training_details`, `data_source_details`, and `input_details` as per your specific data source and requirements.

Run the following commands in your terminal to deploy the infrastructure:

```sh
pulumi up
```

Please ensure you review the preview of the resources that Pulumi is about to create, and confirm by selecting `yes`.

The `pulumi.export` line at the end of the script will output the ID of the anomaly detection job, which you can use to manage or reference the job within your application or for integrating with additional monitoring or notification services.

It is highly recommended to visit the official [Pulumi OCI documentation](https://www.pulumi.com/registry/packages/oci/api-docs/aianomalydetection/) for detailed attributes and configuration options available for each of these resources.