1. Alerts for Drift Detection in Machine Learning Models via Sentry


    Drift detection in machine learning models is a vital aspect of maintaining the reliability of your predictions. As real-world data changes, the assumptions that your model was trained on might no longer hold true. This can result in model predictions becoming less accurate over time.

    One way to monitor and alert upon drift detection is by using Sentry's error logging and alerting capabilities. With Sentry, you can create automated alerts that trigger when certain conditions are met—such as a significant drop in the accuracy of your model predictions.

    To achieve this with Pulumi, you would set up Sentry with a project for your machine learning application, define rules that specify the conditions under which you consider drift to be detected, and create a system for reporting these issues from your machine learning application to Sentry.

    Below is a Pulumi program written in Python that sets up a Sentry project and a rule for drift detection. The rule here is a placeholder; you would customize it depending on the specific metrics you're monitoring for drift.

    import pulumi import pulumi_sentry as sentry # Replace these variables with actual values. organization_slug = "your-sentry-organization-slug" # Create a Sentry team team = sentry.SentryTeam("ml-team", organization=organization_slug, name="Machine Learning Team", slug="machine-learning-team" ) # Create a Sentry project for the machine learning model project = sentry.SentryProject("ml-project", organization=organization_slug, team=team.slug, name="Machine Learning Model", slug="machine-learning-model", platform="python" # Assuming the ML model service is written in Python ) # Define a Sentry rule for drift detection. # The conditions, frequency, and actions will need to be adjusted based on the specific use case. rule = sentry.SentryRule("drift-detection-rule", organization=organization_slug, project=project.slug, action_match="any", frequency=30, # In minutes, the frequency to evaluate the rule. name="Model Drift Detection", conditions=[{ # The conditions under which an alert would be sent. Change as needed. "name": "An example condition", "value": "What to look for", }], actions=[{ # The actions to take when the conditions are met. "name": "Send a notification to the relevant team", "value": "Team's communication channel - could be email, slack, etc.", }] ) # Export the Sentry project URL for quick access pulumi.export('sentry_project_url', pulumi.Output.concat('https://sentry.io/organizations/', organization_slug, '/projects/', project.slug))

    This program sets up the following resources:

    1. Sentry Team: This is the group within your Sentry organization dedicated to a particular stream of work, in this case, machine learning.
    2. Sentry Project: Each project within Sentry can represent an application, service, or part of your codebase. Here, a project is created for your machine learning model.
    3. Sentry Rule: This is a condition-based rule that specifies the criteria for an alert to be fired. You can have rules based on error frequency, message contents, and more.

    Please note that you will need to customize your Sentry rule according to the specific conditions that indicate model drift in your case. The above example uses placeholder values within the rule for illustrative purposes. You might implement custom Sentry logic within your application to send specific messages to Sentry when drift is detected, and then alert appropriate team members so that they can take corrective action.