Production Monitoring and Logging for Amazon Bedrock AI

Building generative AI applications with Amazon Bedrock is only the first step; keeping them reliable, secure, and cost-effective in production requires robust observability. This text-based course guides you through setting up comprehensive monitoring, logging, and tracing for your AI workloads. You will transition from basic API calls to deploying production-ready Bedrock applications backed by deep operational visibility, learning to track token usage, monitor latency, audit prompt-response pairs safely, and manage infrastructure costs effectively. What you will learn: Understand the foundational concepts of AI observability, including latency, token consumption, and model invocation metrics; Configure AWS CloudTrail to audit API calls and ensure compliance for your generative AI workflows; Implement prompt and response logging using Amazon CloudWatch while maintaining data privacy and security; Track and analyze model invocation costs by monitoring input and output token usage; Set up real-time alerts for system anomalies, high latency, and budget thresholds; Monitor Bedrock Guardrails to track blocked inputs and policy violations in production. Starting with core definitions and cloud monitoring fundamentals, this course walks you through step-by-step configuration patterns, code snippets, and architectural best practices. You will learn how to integrate native AWS monitoring tools to build a resilient, transparent AI infrastructure. Designed for software developers, cloud enthusiasts, and system administrators new to AI operations, this course requires no prior experience with machine learning or advanced DevOps. Start reading today to build secure, transparent, and budget-friendly AI applications on AWS.