Serverless and Event-Driven Architecture Components

Good judgment when building scalable, cost-effective, and secure ML systems is crucial. In practice, ML systems must handle unpredictable inference traffic spikes, execute batch processing jobs on schedule, and respond to event-driven retraining triggers, all without overprovisioning infrastructure. Serverless services eliminate the need to manage servers, automatically scale with demand, and charge only for what you use, which makes them a natural fit for production ML workloads.

This lesson covers four serverless building blocks that form the backbone of event-driven ML architectures on AWS. AWS Lambda provides event-driven compute for lightweight inference and preprocessing. Amazon SQS decouples pipeline stages by buffering messages between producers and consumers. Amazon EventBridge routes events from AWS services to targets based on rules, enabling automated retraining and fan-out patterns. AWS Step Functions orchestrates multi-step ML pipelines with built-in retry logic and conditional branching. Each service addresses a distinct architectural concern, and the exam frequently tests your ability to distinguish when one is appropriate vs. another.

AWS Lambda fundamentals and ML patterns

AWS Lambda is a serverless compute service that executes code in response to events without requiring you to provision or manage servers. You upload your function code, define a trigger (such as an S3 object-creation event or an API Gateway request), and Lambda handles the rest, including scaling, patching, and high availability.

Key constraints for ML workloads

Several Lambda constraints directly affect how you design ML integrations.

• Maximum execution timeout of 15 minutes: This hard limit means that Lambda cannot run long-duration model training jobs, but it is more than sufficient for inference routing and data validation tasks.

• Memory allocation up to 10 GB: Lambda allocates CPU power proportional to memory, so memory-intensive preprocessing tasks, such as feature engineering on small datasets, are feasible.

• Ephemeral /tmp storage up to 10 GB: Temporary files, such as downloaded model artifacts, can be stored here during execution, but they do not persist across invocations. ...