SignalFx for Monitoring Machine Learning Model Performance

Question

Pulumi · Accepted Answer

To monitor the performance of your machine learning model, you can use AWS SageMaker's MonitoringSchedule resource, which helps you schedule model monitoring jobs. These jobs provide continuous model monitoring to detect data drift and deviations in model performance. Pulumi enables you to define this infrastructure as code, providing a repeatable and version-controlled setup process.

Below is a Pulumi program written in Python that sets up a monitoring schedule for a machine learning model. Here's what we do in each step:

1. We begin by importing the required Pulumi AWS SDK.
2. We define an AWS SageMaker Model, which represents the model that you've trained and want to deploy.
3. We then define a MonitoringSchedule, which will use the model data to schedule and execute monitoring jobs regularly.

We will not cover the model training process or the creation of the model artifacts needed before setting up the monitoring. This Pulumi program assumes that the training job and model artifacts already exist.

```python
import pulumi
import pulumi_aws as aws

# Define an AWS SageMaker Model (note that actual ARN values, and other specifics would come from your already trained model).
model = aws.sagemaker.Model("my-model",
    execution_role_arn="arn:aws:iam::123456789012:role/SageMakerExecutionRole",  # The ARN of the IAM role associated with your SageMaker model
    primary_container={
        "image": "123456789012.dkr.ecr.us-west-2.amazonaws.com/my-sagemaker-model:latest", # The Docker image of the model hosted in an ECR repository
        "model_data_url": "s3://my-model-artifacts-bucket/model.tar.gz"  # The S3 path to the model artifacts
    })

# Define a SageMaker Endpoint, which is the deployable model wrapped as an endpoint for real-time inference.
endpoint = aws.sagemaker.Endpoint("my-endpoint",
    endpoint_config_name="my-endpoint-config")

# Define the SageMaker Monitoring Schedule to monitor the performance of the model.
# It examines the data going to the model and the predictions it returns.
monitoring_schedule = aws.sagemaker.MonitoringSchedule("my-monitoring-schedule",
    monitoring_schedule_config={
        "monitoring_type": "DataQuality",  # The type of monitoring, in this case monitoring for data quality.
        "schedule_config": {
            "schedule_expression": "cron(0 * ? * * *)"     # Format like UNIX cron syntax, this is set to run hourly.
        },
        "monitoring_job_definition": {
            "baseline_config": {
                "constraints_resource": {
                    "s3_uri": "s3://my-baseline/constraints.json"  # Baseline constraints used for comparison
                },
                "statistics_resource": {
                    "s3_uri": "s3://my-baseline/statistics.json"   # Baseline statistics of the model data
                },
            },
            "monitoring_inputs": [
                {
                    "endpoint_input": {
                        "endpoint_name": endpoint.name,  # Using the endpoint we defined earlier
                        "local_path": "/opt/ml/processing/input",  # The local path where the captured data is located within the container
                        "s3_input_mode": "File",
                        "s3_data_distribution_type": "FullyReplicated",
                    }
                }
            ],
            "monitoring_output_config": {
                "monitoring_outputs": [
                    {
                        "s3_output": {
                            "s3_uri": "s3://my-monitoring-outputs/monitoring-schedule",  # The S3 location where monitoring data will be stored
                            "local_path": "/opt/ml/processing/output",
                            "s3_upload_mode": "EndOfJob"
                        }
                    }
                ]
            },
            "monitoring_resources": {
                "cluster_config": {
                    "instance_count": 1,      # The number of instances to use for the monitoring job
                    "instance_type": "ml.m5.large",  # The type of instance to use for the monitoring job
                    "volume_size_in_gb": 30,  # The size, in GB, of the EBS volume to attach to the instance
                }
            },
            "role_arn": model.execution_role_arn  # Reusing the role defined above
        }
    })

# Export the names of the resources we've just created, for use in other programs or reference.
pulumi.export('model_name', model.name)
pulumi.export('endpoint_name', endpoint.name)
pulumi.export('monitoring_schedule_name', monitoring_schedule.name)
```

[Learn more about SageMaker Model Monitoring](https://www.pulumi.com/registry/packages/aws/api-docs/sagemaker/monitoringschedule/)

This is a basic setup and may not cover your specific use case completely. For a production setup, there are several other considerations and configurations to take into account, such as security settings, network configuration, resource fine-tuning, appropriate IAM permissions, and more. You'll need to adjust your Pulumi code to match your production environment's specific requirements.