Summary
Full Transcript
AWS Certified AI Practitioner Exam — Domain 3: Applications of Foundation Models Welcome to Video 7 of our AWS Certified AI Practitioner series! In this video, we explore how to design intelligent applications using foundation models. From model selection and inference tuning to Retrieval Augmented Generation (RAG) and agent-based automation, this video covers everything you need to know to build scalable AI solutions on AWS. 📚 In this video, you will learn: ✅ How to choose the right foundation model based on cost, modality, latency, and multilingual support 🔍 The impact of inference parameters like temperature and token limits on model responses 📖 What is Retrieval Augmented Generation (RAG) and how it enhances model accuracy using external knowledge 🗃️ AWS services for storing embeddings in vector databases (OpenSearch, Aurora, Neptune, DocumentDB, RDS) 💰 Cost tradeoffs between pre-training, fine-tuning, in-context learning, and RAG 🤖 Role of agents in multi-step tasks using Agents for Amazon Bedrock 💡 Key Takeaways: Learn how to design, customize, and scale foundation model-based applications using AWS tools and best practices. 📺 Watch Next: Video 8 will cover Effective Prompt Engineering Techniques — including chain-of-thought, few-shot learning, prompt templates, and risks like jailbreaking and prompt poisoning. 🔔 Subscribe to stay updated with the full guided series and ace your AWS Certified AI Practitioner exam! 👍 Like, Share, and Comment if this video helped you understand how to apply foundation models in real-world scenarios. #AWS #AIPractitioner #FoundationModels #GenerativeAI #RetrievalAugmentedGeneration #AmazonBedrock #PromptEngineering #AWSCertification #CloudComputing #MachineLearning #AIApplications
Continue this lesson in the app
Install CourseHive on Android or iOS to keep learning while you move.
