Generative AI and Amazon Bedrock
Description
Comprehensive understanding of generative AI concepts, foundation models, and Amazon Bedrock for building AI applications.
Learning Objectives
Learners will master generative AI fundamentals including foundation models, large language models, prompt engineering, and retrieval-augmented generation (RAG). They will understand Amazon Bedrock platform, model selection, fine-tuning strategies, responsible AI practices, and integration patterns for building generative AI applications. Students will learn to implement guardrails, optimize performance, and deploy scalable generative AI solutions.
Topics (10)
Comprehensive Bedrock platform coverage including model catalog, API access patterns, security configuration, and integration with other AWS services.
Advanced prompt engineering including prompt structure, few-shot learning, chain-of-thought prompting, and optimization techniques for various use cases.
Comprehensive RAG implementation including knowledge base creation, retrieval strategies, context integration, and performance optimization.
Advanced fine-tuning strategies including parameter-efficient fine-tuning, domain adaptation, and custom model development for specific applications.
Comprehensive responsible AI implementation including bias detection, content moderation, ethical guidelines, and safety mechanisms for generative AI systems.
Advanced multi-modal AI including vision-language models, text-to-image generation, audio processing, and integrated multi-modal applications.
Advanced optimization including model compression, caching strategies, batch processing, and scalable architecture design for generative AI applications.
Comprehensive integration patterns including API design, workflow automation, user interface integration, and enterprise system connectivity.
Comprehensive overview of foundation models including transformer architectures, training methodologies, model capabilities, limitations, and use case selection.
Complete application development including architecture design, deployment strategies, monitoring, governance, and maintenance of production generative AI systems.