This unit introduces the current generative AI landscape, core prompt engineering practices, and an overview of Amazon Bedrock and multimodal foundational models. Students will also learn essential security and privacy considerations, project planning principles for organizational deployments, and set up a local development environment (Python, Jupyter/ LangChain, AWS CLI) to prepare for hands-on Bedrock work in later units.
Learning Objectives
- Analyze the current generative AI landscape and multimodal model capabilities to identify appropriate use cases and limitations for organizational projects
- Apply prompt engineering techniques to design, test, and iterate effective prompts across text and multimodal scenarios
- Demonstrate the installation and basic configuration of a development environment for Bedrock integration, including Python, Jupyter Notebook with LangChain, and the AWS CLI, and verify API connectivity
- Evaluate security, privacy, and end-to-end project planning considerations and produce a preliminary deployment checklist for generative AI solutions
Curriculum
- 1 Section
- 9 Lessons
- 30 hours
Expand all sectionsCollapse all sections
- Unit 1 — Generative AI Foundations with Bedrock9
- 1.1The Current Generative AI Landscape20 mins
- 1.2Crafting Good Prompts20 mins
- 1.3Overview of Using the Amazon Bedrock API20 mins
- 1.4Multimodal Foundational Models20 mins
- 1.5Security and Privacy for Generative AI Solutions20 mins
- 1.6Planning for Organizational, End-to-End Projects with Generative AI20 mins
- 1.7Installing Python20 mins
- 1.8Installing Jupyter Notebook and LangChain20 mins
- 1.9Installing the AWS Command-Line Interface (AWS CLI)20 mins
Instructor
Free
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