I encountered an issue while setting up Vertex AI Model Monitoring using the aiplatform SDK, specifically when configuring BatchPredictionJob.create(). The documentation was unclear and lacked examples for defining model_monitoring_objective_config and model_monitoring_alert_config. This caused compatibility issues due to incomplete references and unclear parameter mappings.
The main confusion arose because these configurations are not well-documented in the SDK and required exploring the SDK source code to understand the correct structure. Additionally, the SDK requires configurations from aiplatform.model_monitoring, which isn’t explicitly clear in the official guides.
What I Tried:
I initially referred to the SDK documentation and attempted various configurations based on what seemed logical, assuming all components could be configured directly using the SDK’s classes. However, this caused multiple type and parameter mismatch errors.
What I Expected:
I expected clear and straightforward documentation showing how to define and pass monitoring configurations when creating a batch prediction job using aiplatform.BatchPredictionJob.create().
What Actually Happened:
I encountered errors due to parameter mismatches between the SDK and GAPIC APIs. After exploring the source code, I realized that the required configurations must be defined using aiplatform.model_monitoring classes. This cross-library dependency was not documented.
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I ran into an issue while setting up Vertex AI Model Monitoring using the aiplatform SDK, specifically when configuring BatchPredictionJob.create(). The challenge was understanding how to pass model_monitoring_objective_config and model_monitoring_alert_config, as the SDK documentation lacked clear examples.
Solution:
To resolve this, I used the following setup:
-
Import the Required Libraries:
from google.cloud import aiplatform -
Define Model Monitoring Configurations:
# Define skew detection configuration objective_config = aiplatform.model_monitoring.ObjectiveConfig( skew_detection_config=aiplatform.model_monitoring.SkewDetectionConfig( skew_thresholds={"feature1": 0.3}, data_source="gs://my-data-classification/train_data_new.csv", target_field="type", data_format="csv", ) ) # Define email alert configuration alert_config = aiplatform.model_monitoring.EmailAlertConfig( user_emails=["[email protected]"] ) -
Create the Batch Prediction Job:
batch_job = aiplatform.BatchPredictionJob.create(
job_display_name="vertex_monitoring_job",
model_name="projects/your_project_id/models/your_model_id",
instances_format="jsonl",
predictions_format="jsonl",
gcs_source=["gs://your-input-data/input.jsonl"],
gcs_destination_prefix="gs://your-output-data/",
machine_type="n1-standard-4",
model_monitoring_objective_config=objective_config,
model_monitoring_alert_config=alert_config,
)
print(f"Batch job created: {batch_job.resource_name}")
This resolved the issue, and the job was successfully created with model monitoring enabled.
The main challenge was understanding that the model_monitoring_objective_config and model_monitoring_alert_config need to be constructed using aiplatform.model_monitoring classes. I hope this helps anyone facing similar confusion!
For more details on creating a complete batch prediction pipeline with Vertex AI, you can refer to this blog post I wrote.
Raj Mudigonda is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.