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AI-Powered Analytics and Performance Engineering

This course is part of AI Tooling Specialization

Instructors: Alfredo Deza

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

3 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

3 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Build Rust-Bedrock analytics pipelines, use GenAI for Python-to-Rust code transformation, and construct performance instrumentation pipelines on AWS
Benchmark Lambda functions across Python and Rust using real workload data, analyze cost profiles with Claude, and prepare analytics data

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the AI Tooling Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

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.