Learn to build AI-powered analytics pipelines on AWS using Amazon Bedrock, Lambda benchmarking, and Amazon Q for business intelligence. You will explore how Bedrock integrates with Rust for high-performance analytics, calling foundation model APIs from serverless architectures with token-level scaling. The course covers building Rust-Bedrock analytics pipelines that combine model invocation with data processing, and using generative AI to convert Python code to Rust for performance-critical workloads. You will construct intelligent code transformation pipelines that automate language migration, add performance instrumentation with GenAI, and build end-to-end AWS performance pipelines from instrumentation to analysis. The benchmarking module demonstrates real-world Lambda cost comparison between Python and Rust using synthetic Fortune 500 workloads, showing 10x cost differences at scale with three billion monthly invocations. You will use SageMaker DataWrangler for analytics data preparation and explore energy efficiency considerations for AI workloads. The Amazon Q module covers transforming raw data into living actionable insights through automatic anomaly detection, natural language processing that converts questions into SQL and Python queries, and CodeCatalyst dev environments for analytics projects. By completing this course, you will be able to build Rust-Bedrock analytics pipelines, benchmark Lambda performance for cost optimization, and use Amazon Q for AI-powered business intelligence.