AI Value - FinOps AI
- Código del Curso FINOPS-AI
- Duración 1 Día
Otros Métodos de Impartición
Salta a:
Método de Impartición
Este curso está disponible en los siguientes formatos:
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Cerrado
Cerrado
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Clase de calendario
Aprendizaje tradicional en el aula
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Aprendizaje Virtual
Aprendizaje virtual
Solicitar este curso en un formato de entrega diferente.
Temario
Parte superiorMaster the true cost of AI in production - Scale AI Prod without "Bill Shock"
As AI flows into each company, there is an urgent need to avoid: "for every $1 of revenue generated, $ 4 spent on infrastructure! ".
This course will help you master concepts, use cases, and cost drivers for AI value.
During the training you will use a pragmatic framework as an optimization lever and get inspiration from actionable use cases: lower costs, higher ROI.
Updated 09/10/2026
Virtual Learning
This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins.
Calendario
Parte superiorDirigido a
Parte superiorThis course is targeting all people interested to discover how to understand and measure the AI Value.
Objetivos del Curso
Parte superiorAfter this course, participants should be able to:
- Gain expertise in optimized AI cost management
- Translate complex AI cost structures into reliable insights
- Take informed strategic decisions (KPIs, governance)
Contenido
Parte superiorModule 1:
- Strategic introduction: challenges, ecosystem, multimodal, conceptual basics
- Tokenization, inference (input/output), prompts, benchmarking...
- AI Architecture, tech stacks, Tokenonomics (cost per Token, KV cache...)
Module 2:
- AI FinOps Framework architecture, rate optimizations, usage, licenses, benchmarks
- Unit Economics and KPIs for ROI calculations and associated Operating Model
- Governance patterns: context engineering, prompt, RAG, fine-tuning, linear > agentic
Module 3:
- Use case #1 - LLM + context vs. LLM + RAG: injection vs. indexing cost comparison
- Use case #2 - SLM vs. LLM: quant/qual comparison on a Business IT process
- Use case #3 - A/B workflow comparisons: models, injection, guardrails...
Pre-requisitos
Parte superiorParticipants should have some level of awareness about AI and Finances mechanisms and concepts like KPIs, ROI.
Certificación de Prueba
Parte superiorNo certification
Siguientes Cursos Recomendados
Parte superiorNo specific recommendation
Más información
Parte superior- Course book provided to participants
