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Explore Advanced AI Optimization with AWS and Captial One with 2,873 views, 60 likes, and 1 comments. Experience the impact of this video content that has captured audience attention.

In this 300-level session, AWS's Steph Gooch and Adam Richter, joined by Capital One Senior Distinguished Engineer Brent Segener, structure advanced AI cost optimization around a London Underground twist: "See it, Save it, Sort it." Steph covers seeing your Amazon Bedrock spend, with IAM-principal cost allocation in CUR 2.0, inference profiles, and enabling Bedrock logging for token-level observability. Adam dives deep into saving it: picking the right model (modality, strengths, evals, open-weight, distillation, mixture-of-experts, prompt routing), then a rich playbook for optimizing input tokens (lexical filtering, compression, rolling context, summarization, minimal context) and output tokens (max tokens, output type, system prompts, few-shot, task decomposition). Brent brings the Capital One GPU view: why training is a scheduling problem and inference is a sizing problem, tracking DCGM metrics and shifting to tokens as the unit of work, sharing capacity reservations across accounts, and using SageMaker real-time inference and FMBench. Steph closes on sorting it: observability, governance with service control policies, code defaults, and AI-assisted FinOps. 00:00 Intro: See It, Save It, Sort It 02:10 See It: Bedrock Visibility and Logging 12:30 Save It: Model Selection and Distillation 21:14 Save It: Input and Output Token Optimization 32:17 GPU Optimization at Capital One 40:43 Sort It: Governance, Defaults, and AI for FinOps Watch the Day 1 Keynote and Tokenomics Foundation announcement: https://www.youtube.com/live/W4g8xvduIbs Watch the Day 2 Keynote: https://www.youtube.com/live/vyQTW0PLw7A Learn more about Token Economics: https://tokeneconomics.com https://x.finops.org https://join.finops.org #FinOps #FinOpsX #AWS #Bedrock #AICostOptimization

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