1. Logging and Error Tracking for AI Applications in Cloud Environments


    Logging and error tracking are essential aspects of application monitoring, which provide insights into how applications are performing and help quickly resolve any issues arising in production. In a cloud environment, logging and error tracking can be especially complex due to the distributed nature of the services. However, cloud providers offer various managed services and tools for handling these necessary tasks efficiently.

    In this context, let’s design a logging and error tracking system for AI applications using Google Cloud Platform (GCP) services—which are popular choices for AI and cloud-native applications.

    The key components we’ll use in GCP for this purpose are:

    1. Stackdriver Logging (Cloud Logging): This managed service allows you to store, search, analyze, monitor, and alert on log data and events from GCP and Amazon Web Services (AWS). It helps you understand what’s happening with your applications and services.

    2. Stackdriver Error Reporting (Cloud Error Reporting): This service automatically counts, analyzes, and aggregates the crashes in your running cloud services. It alerts you when new errors are detected.

    3. Pub/Sub: A scalable, durable event ingestion and delivery system that allows you to quickly integrate systems hosted on GCP as well as externally.

    Here’s a simple Pulumi Python program that sets up logging and error tracking for an AI application in GCP using these components. The program comments will guide you through what each part is doing.

    import pulumi import pulumi_gcp as gcp # A log-based metric resource to track the performance and errors. You can create custom # metrics based on log data. For example, you might want to track the rate of error # messages related to your AI application. ai_app_log_metric = gcp.logging.Metric("ai_app_log_metric", filter="resource.type=global AND severity>=ERROR", metric_descriptor=gcp.logging.MetricMetricDescriptorArgs( metric_kind="DELTA", value_type="INT64", labels=[ # Define the structure of the data that will be logged gcp.logging.MetricMetricDescriptorLabelArgs( key="job_id", value_type="STRING", description="The identifier of the AI job", ), ], ), description="Metric for tracking AI application errors" ) # An Error Reporting configuration to track and group the exceptions thrown by an AI application. # This high-level resource will automatically capture and report errors from the application. ai_app_error_reporting = gcp.errorreporting.ProjectEventConfig("ai_app_error_reporting", project=pulumi.config.require("gcp:project"), event_configs=[ gcp.errorreporting.ProjectEventConfigEventConfigArgs( event_type="python", service="", # Specify your AI application's service identifier here ) ], ) # A Pub/Sub topic where log entries will be published. This provides a way to create an # event-driven architecture to trigger follow-up actions, such as sending notifications or # streaming logs to a third-party monitoring tool. log_topic = gcp.pubsub.Topic("log_topic") # A subscription to the Pub/Sub topic. This scenario demonstrates setting a push endpoint, # which might be an HTTP triggered cloud function or cloud run service that processes log data. log_topic_subscription = gcp.pubsub.Subscription("log_topic_subscription", ack_deadline_seconds=20, topic=log_topic.name, push_config=gcp.pubsub.SubscriptionPushConfigArgs( push_endpoint="https://example.com/push-handler" # Replace with your actual handler endpoint ), ) # Output the names of the created resources pulumi.export("log_metric_name", ai_app_log_metric.name) pulumi.export("error_reporting_config", ai_app_error_reporting.service) pulumi.export("pubsub_topic_name", log_topic.name) pulumi.export("pubsub_subscription_name", log_topic_subscription.name)

    This Pulumi program performs the following tasks:

    • It initializes a log-based metric to track errors with severity level ERROR or higher. You can customize the filter attribute to match specific log entries or errors from your AI application logs. The metric descriptor labels are used to provide additional context for the logs, such as including a job ID to link logs to specific AI operations.

    • It sets up Error Reporting for Python applications. When running your AI applications on GCP services like Cloud Functions, App Engine, or Compute Engine, this will automatically group and track exceptions. Configure the service attribute with the identifier of your AI application service to categorize errors accordingly.

    • It creates a Pub/Sub topic and subscription to demonstrate how you can stream logs to a real-time processing or error-handling service. Pub/Sub is configured here with a push_config to send the messages to an HTTP endpoint, which you can replace with the endpoint up handling your error processing logic or forwarding logs to another system or team.

    You can define additional resources or refine these definitions to fit specific needs or use cases. Remember to replace placeholders like the push endpoint URL with actual values when deploying your infrastructure.

    Always refer to the Pulumi GCP Documentation for details about the properties and usage of the resources mentioned in the Pulumi program.