Achievements | Philip Rehberger

CourseFundamentalCompleted

Generative BI with Amazon Q in Quicksight - Getting Started

Amazon Q in QuickSight introduces a new suite of business intelligence (BI) capabilities by using the large language models (LLMs) of Amazon Bedrock and combining them with the capabilities of Amazon QuickSight. In this course, you will learn about technical concepts and the benefits of using Amazon Q in QuickSight. You will learn about the architecture of Amazon Q in QuickSight and how the built-in features help you to build dashboards and derive insights from your data with natural language queries. Course level: Fundamental. Duration: 60 minutes. This course includes presentations, demonstrations, and assessments.

AWSMar 2026

Certifications,
courses, and more

Building cost-effective RAG applications with Amazon Bedrock Knowledge Bases and Amazon S3 Vectors

Let's Talk

Want to work together?

Building Generative AI Applications Using Amazon Bedrock

Building Production-Ready Agents with TypeScript, Effect, and Amazon Bedrock

Building Production-Ready AI Agents with Amazon Bedrock AgentCore

Building Safe and Responsible Generative AI Applications with Amazon Bedrock Guardrails

Building with MultiModal Knowledge Bases in AWS Bedrock

Building Your Agentic Applications the Well-Architected Way

Designing Secure Retrieval Augmented Generation (RAG) Applications with AWS

Evaluate and Enhance RAG Performance with Amazon Bedrock

Exploring Amazon Nova models using Amazon Bedrock

Fundamentals of Generative AI

Generative BI with Amazon Q in Quicksight - Getting Started

Increasing Application Security with Bedrock Guardrails and GenAI

Integrating Amazon Bedrock powered Agents with Model Context Protocol Servers using the Strands Agents SDK

Introduction to Agentic AI on AWS

Introduction to Amazon Bedrock: Foundation Models and AI Applications

Introduction to Generative AI - Art of the Possible

Kiro Getting Started

Mastering Model Evaluation in Amazon Bedrock: From Basics to Best Practices

Prompt Engineering Best Practices for Amazon Bedrock Models

Query Amazon Aurora PostgreSQL using Amazon Bedrock Knowledge Bases structured data

Spec-Driven Development with Kiro

Streamline GitHub workflows with generative AI using Amazon Bedrock and MCP

Using Amazon Q to Enhance Applications and Developer Productivity

Certifications,courses, and more

Building cost-effective RAG applications with Amazon Bedrock Knowledge Bases and Amazon S3 Vectors

Let's Talk

Want to work together?

Building Generative AI Applications Using Amazon Bedrock

Building Production-Ready Agents with TypeScript, Effect, and Amazon Bedrock

Building Production-Ready AI Agents with Amazon Bedrock AgentCore

Building Safe and Responsible Generative AI Applications with Amazon Bedrock Guardrails

Building with MultiModal Knowledge Bases in AWS Bedrock

Building Your Agentic Applications the Well-Architected Way

Designing Secure Retrieval Augmented Generation (RAG) Applications with AWS

Evaluate and Enhance RAG Performance with Amazon Bedrock

Exploring Amazon Nova models using Amazon Bedrock

Fundamentals of Generative AI

Generative BI with Amazon Q in Quicksight - Getting Started

Increasing Application Security with Bedrock Guardrails and GenAI

Integrating Amazon Bedrock powered Agents with Model Context Protocol Servers using the Strands Agents SDK

Introduction to Agentic AI on AWS

Introduction to Amazon Bedrock: Foundation Models and AI Applications

Introduction to Generative AI - Art of the Possible

Kiro Getting Started

Mastering Model Evaluation in Amazon Bedrock: From Basics to Best Practices

Prompt Engineering Best Practices for Amazon Bedrock Models

Query Amazon Aurora PostgreSQL using Amazon Bedrock Knowledge Bases structured data

Spec-Driven Development with Kiro

Streamline GitHub workflows with generative AI using Amazon Bedrock and MCP

Using Amazon Q to Enhance Applications and Developer Productivity

Certifications,
courses, and more