Advanced AI Analytics on AWS: Amazon Bedrock, Q, SageMaker Data Wrangler, and QuickSight
1h 10mAdvanced2025-03-12
Authors
Noah Gift
MLOps Expert | Solopreneur | Author | Adjunct Professor | CTO

Pragmatic AI Labs
Course details
Discover how to elevate your data analytics skills using AI-enhanced tools on AWS. In this course, MLOps expert Noah Gift shows you how to integrate Amazon Bedrock for advanced code analysis and Amazon SageMaker Data Wrangler for efficient data processing. Find out how to use Amazon Q and QuickSight. Explore practical examples and real-world scenarios where AI can optimize your existing workflows and reduce costs significantly. Learn how to automatically detect anomalies, generate visual stories, and create a comprehensive data narrative. Step through the methodology for leveraging AI to enhance traditional processes and improve overall performance. This course provides valuable insights into making your analytics pipeline more efficient and cost-effective. When you complete the course, you'll be equipped to apply AI tools to drive decision-making and achieve better business outcomes.
Skills covered
Amazon QAmazon BedrockAmazon SageMakerCloud DevelopmentData EngineeringAmazon Web Services (AWS)AmazonGenerative AIArtificial Intelligence FoundationsCloud ServicesData AnalysisArtificial Intelligence (AI)Cloud ComputingData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOne-Off
Concepts
Introduction and Overview
- 01 - Course introduction and overview
1. AWS AI Services and Integration Fundamentals
- 02 - Introduction to analytics with AI on AWS
- 03 - Visualizing Rust and Bedrock analytics integration
- 04 - Hands-on demo - Bedrock analytics with Rust
- 05 - Converting Python analytics code to Rust using GenAI
- 06 - Building an intelligent code transformation pipeline
- 07 - Implementing code instrumentation with GenAI on AWS
- 08 - Performance pipeline integration with GenAI
2. Performance Optimization and Analytics Tools
- 09 - Analyzing lambda costs - Rust vs. traditional approaches
- 10 - Benchmarking lambda performance - Rust vs. Python with Claude
- 11 - Leveraging AWS Data Wrangler for analytics
- 12 - Optimizing energy efficiency in AI analytics workloads
- 13 - Creating living insights with Amazon Q AI analytics
- 14 - Setting up development environments with Amazon Q code catalyst
- 15 - Translating analytics workflows with Q - Python CLI demo
Conclusion
- 16 - Course summary and next steps
Related courses
- Natural Language Processing (NLP) on Amazon Bedrock
- Text to SQL: Amazon Redshift Serverless for Generative SQL in Amazon Q
- Hands-On Data Analysis with ChatGPT-5
- How to Use AI Reasoning Models: Practical Applications with Hands-On Exercises
- The AI-Driven Product Designer
- Complete Guide to Google BigQuery for Data and ML Engineers
- Microsoft Copilot for Excel
- AI-Assisted Analytics Engineering with dbt Copilot
Related learn paths
- Advance Your Data Skills in Apache Spark
- Advance Your Skills in AI and Machine Learning
- Become an IT Security Specialist
- Explore a Career as a Cloud Administrator
- Building Generative AI Skills for Web Developers
- Hands-On Healthcare Analytics
- AI for Healthcare: Essentials for Technical Roles
- MLOps Essentials for Developers and AI Engineers: Tools, Pipelines, Security