Summary
Full Transcript
In this video, we explore Operational Efficiency and Optimization for Generative AI systems on AWS, with a strong focus on Amazon Bedrock and exam-relevant best practices. This session covers how to control cost, improve performance, and scale generative AI workloads by optimizing tokens, selecting the right foundation models, and using proven AWS patterns. What You’ll Learn Why token efficiency is the fastest way to reduce GenAI costs How to measure token usage using Amazon Bedrock Count Tokens API and CloudWatch Context pruning, RAG optimization, and conversation history management Cost-effective model selection and Intelligent Prompt Routing in Bedrock How batching and batch inference improve throughput Caching strategies: semantic caching, prompt caching, and edge caching Techniques for improving latency and responsiveness, including streaming responses Key inference parameters: Max Tokens, Temperature, Top-P, and Top-K Reliability patterns such as exponential backoff and connection pooling Amazon Bedrock cross-region inference for high availability and throughput Who This Video Is For AWS certification candidates (Generative AI, ML, Data Engineer, Solutions Architect) Engineers building production GenAI systems on AWS Architects designing scalable, cost-efficient AI workloads Anyone preparing for AWS exam scenarios involving Bedrock This video is part of a Generative AI on AWS learning series, designed to explain not just what the services do—but why and when to use them in real-world architectures and exam questions. 👍 If you found this helpful, like the video, subscribe for more AWS & GenAI deep dives, and check out the full series. #AWSGenerativeAI #AmazonBedrock #AWSAIOptimization #GenAICostOptimization #TokenEfficiency #RAGOptimization #BedrockCaching #AWSCertification #AWSAIExamPrep #GenerativeAIArchitecture #AWSMachineLearning
Continue this lesson in the app
Install CourseHive on Android or iOS to keep learning while you move.
