Amazon Bedrock & Q Developer for Python: Build Generative AI Applications with Amazon Bedrock, Amazon Q Developer, and Python

CourseDIY

$69.99

Video Jun 2026 4hrs 59mins 1st Edition

CourseDIY

$69.99

Video Jun 2026 4hrs 59mins 1st Edition

What do you get with a video?

Download this video in MP4 format

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

View table of contents

Key benefits

Comprehensive coverage of Amazon Bedrock, Amazon Q Developer, and AWS GenAI services
Clear explanation of why prompts, model selection, and tuning shape AI outcomes
Practical Python workflows showing how to build, test, and troubleshoot AI solutions on AWS

Description

Generative AI is reshaping modern application development, and AWS provides powerful tools to build AI-driven solutions. This course introduces Amazon Bedrock and Amazon Q Developer, covering model access, Bedrock playgrounds, and AI-assisted development workflows. You will explore core concepts such as AI, generative AI, machine learning, deep learning, foundation models, large language models, and prompt engineering. Through hands-on examples, you will work with Amazon Titan, Claude, Llama, and Stability AI models for text and image generation, while learning how Amazon Q Developer integrates with AWS Lambda and Amazon Bedrock. The course concludes with model configuration and troubleshooting. You will learn to use inference parameters such as temperature, Top P, Top K, maximum length, system prompts, and stop sequences, while resolving common issues including access, validation, timeout, and permission errors. By the end of this course, you will be able to build, configure, and troubleshoot generative AI applications using Amazon Bedrock, Amazon Q Developer, and Python on AWS.

Who is this book for?

This course is ideal for Python developers, AWS developers, cloud engineers, AI developers, and technical professionals who want to start building generative AI solutions using AWS. Basic knowledge of Python, AWS services, and cloud computing concepts is recommended. Prior experience with machine learning is helpful but not required.