Observability for AI Model Inference Pipelines on Honeycomb

Question

Pulumi · Accepted Answer

To implement observability for AI Model Inference Pipelines on Honeycomb, you would need to integrate your AI pipelines with Honeycomb. Honeycomb is a service used for debugging and observing production systems. It allows you to send data from your application that can be analyzed to understand the performance and behavior of your system. Pulumi doesn't directly support Honeycomb as a standalone provider, but you can integrate observability into your infrastructure using AWS services with AppRunner Observability Configuration and SageMaker Pipeline Monitoring, which can then forward the data to Honeycomb.

Below is a Pulumi program that demonstrates how to set up a SageMaker Inference Pipeline with observability features enabled, which you could then configure to send data to Honeycomb:

1. Set up a SageMaker Model Inference endpoint.
2. Attach AWS CloudWatch logging to monitor the performance.
3. Forward logs and metrics from CloudWatch to Honeycomb using an agent or service integration (note: this part is conceptual as it depends on Honeycomb's integration mechanisms and is not directly built into the Pulumi program).

Remember that while this Pulumi program sets up the resources needed to observe an AI Model Inference Pipeline, to complete the observability through Honeycomb, you'll need to work with the Honeycomb API or a Honeycomb agent to send your data from AWS to Honeycomb itself.

```python
import pulumi
import pulumi_aws as aws

# Set up the IAM role for SageMaker to access AWS resources
sagemaker_role = aws.iam.Role("sagemakerRole",
                              assume_role_policy="""{
                                "Version": "2012-10-17",
                                "Statement": [{
                                    "Action": "sts:AssumeRole",
                                    "Effect": "Allow",
                                    "Principal": {
                                        "Service": "sagemaker.amazonaws.com"
                                    }
                                }]
                              }""")

# Attach policies for SageMaker execution and CloudWatch logging
aws.iam.RolePolicyAttachment("sagemakerRoleAttachPolicy",
                             role=sagemaker_role.name,
                             policy_arn="arn:aws:iam::aws:policy/AmazonSageMakerFullAccess")

aws.iam.RolePolicyAttachment("sagemakerCloudWatchRoleAttach",
                             role=sagemaker_role.name,
                             policy_arn="arn:aws:iam::aws:policy/CloudWatchFullAccess")

# Set up the SageMaker Model Inference endpoint configuration
model = aws.sagemaker.Model("model",
                            execution_role_arn=sagemaker_role.arn,
                            # (Assuming you have a container image that serves your model hosted in ECR)
                            primary_container={
                                "image": "123456789012.dkr.ecr.us-west-2.amazonaws.com/your-model:latest"
                            })

endpoint_config = aws.sagemaker.EndpointConfiguration("endpointConfig",
                                                      production_variants=[{
                                                          "variant_name": "default",
                                                          "model_name": model.name,
                                                          "initial_instance_count": 1,
                                                          "instance_type": "ml.t2.medium"
                                                      }])

# Create the SageMaker Model Inference endpoint
endpoint = aws.sagemaker.Endpoint("endpoint",
                                  endpoint_config_name=endpoint_config.name)

# Export the endpoint name to be accessed outside of Pulumi
pulumi.export("sagemaker_model_endpoint_name", endpoint.endpoint_name)

# At this point, you need to configure the exportation of logs from CloudWatch
# to Honeycomb using Honeycomb's agents or API, which is not covered by Pulumi resources.
# For example, setting up Honeycomb as a destination for CloudWatch Logs.
```

This program performs the following actions:

1. Creates an IAM Role (`sagemakerRole`) for SageMaker to access necessary AWS resources.
2. Attaches two policies to the IAM Role to grant full access to SageMaker services and CloudWatch for logging.
3. Defines a SageMaker Model (`model`) with a container image which serves as the AI inference model.
4. Establishes an Endpoint Configuration (`endpointConfig`) which specifies how the model is served (instance type, initial instance count, etc.).
5. Deploys a SageMaker Endpoint (`endpoint`) which serves the model for client applications to consume for inference.

To connect this setup with Honeycomb, you would need to configure AWS CloudWatch to forward logs and metrics to Honeycomb. This isn't covered in the Pulumi program since it would require setting up a Honeycomb agent or using the Honeycomb API, which is beyond the scope of Pulumi's capabilities.

Remember to replace placeholders like `123456789012.dkr.ecr.us-west-2.amazonaws.com/your-model:latest` with your actual Docker image URI and adjust the instance count and type as per your model's requirements.

To learn more about integrating AWS services with Honeycomb, you would need to refer to Honeycomb's documentation and possibly leverage AWS Lambda functions or other forwarding mechanisms.