GENAI-ENTERPRISE.AA1
Generative AI for Enterprise
Master Generative AI deployment, scaling, and ethical integration for robust enterprise solutions, avoiding common pitfalls.
- Practice in 34 Laboratorios prácticos — nothing to install
- 18 Lecciones interactivas y 201 topics mapped to the official exam objectives
- 335 Preguntas del examen de práctica
Beginner A tu propio ritmo · 1 año de acceso
34 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
18Lecciones interactivas
201Topics
34Laboratorio en vivo
335Preguntas del examen de práctica
18Vídeos
76Tarjetas didácticas
76Glosario de términos
01 / Habilidades que obtendrás
What you will be able to do
Try Free →
No se requiere tarjeta de crédito
This course cuts through the hype, equipping you to implement Generative AI in real enterprise environments. We tackle the hard problems: scaling LLMs, managing costs, and building secure, responsible AI systems.
You'll learn practical strategies for prompt engineering, fine-tuning, and RAG architectures, understanding their limitations.
We cover operationalizing AI, from deployment patterns to ethical governance frameworks. Expect to confront trade-offs between performance, cost, and security, preparing you for the complexities of enterprise AI. This isn't about theoretical perfection; it's about delivering tangible value.
- Architecting and deploying scalable Generative AI solutions within complex enterprise infrastructures, understanding the trade-offs between various deployment patterns and model sourcing strategies.
- Implementing advanced Prompt Engineering and Fine-Tuning techniques to optimize Large Language Models (LLMs) for specific enterprise domains, recognizing the inherent challenges in achieving domain expertise.
- Designing and operationalizing Responsible AI frameworks, including governance, safety guardrails, and ethical dimensions, to mitigate risks and ensure compliant Generative AI adoption.
- Developing and managing Retrieval-Augmented Generation (RAG) systems and Multi-Modal Multi-Agentic frameworks to enhance AI accuracy, reduce hallucinations, and orchestrate complex AI workflows efficiently.
Course Highlights
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18 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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34 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
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335 Preguntas de práctica Pruebas de evaluación con justificaciones de respuesta detalladas
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1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
18 Lecciones interactivas · 201 topics01 The Rise of Generative AI in Enterprises 11 topics · 4 Laboratorio en vivo +
- Evolution of Generative Artificial Intelligence
- Historical and Theoretical Foundations of Generative AI
- The Core Philosophy Behind Generative AI
- How Generative AI Thinks: From Input to Creation
- Where GenAI Creates Value in the Enterprise
- Enterprise Use-Case
- Inside the Architecture of Generative AI Systems
- Hands-On Lab: Experimental Setup
- Challenges and Opportunities
- Key Takeaways
- Reflection Questions
4 Laboratorio en vivo in this lesson — see the labs panel →
02 Scaling and Operationalizing Generative AI 11 topics · 3 Laboratorio en vivo +
- Hands-On Lab: Experimental Setup
- Challenges of Model-Specific Scaling
- Model Sourcing and Deployment Strategies
- Five Dimensions of Model Scale
- LLMOps: The Operational Backbone Of Enterprise-Scale AI
- Data Management in Production
- Integrating Model Governance and Observability
- Future Trends in Scalable Production
- Business Objectives of Using Large Language Models (LLMs)
- Key Takeaways
- Reflection Questions
3 Laboratorio en vivo in this lesson — see the labs panel →
03 Scaling and Managing Generative AI Models in the Enterprise 13 topics · 3 Laboratorio en vivo +
- Understanding the Model Landscape
- Key Decision Factors for Enterprises
- Strategic Implications
- Model Sourcing and Selection
- Hands-On Lab: Experimental Setup
- Data Management: The Foundation of AI Performance
- Model Evaluation, Fine-Tuning, and Optimization
- Model Orchestration, Observability, and Governance
- Production-Grade Scaling and Enterprise Readiness
- Model Observability
- Model Governance
- Key Takeaways
- Reflection Questions
3 Laboratorio en vivo in this lesson — see the labs panel →
04 Responsible AI 12 topics · 3 Laboratorio en vivo +
- Operationalizing Responsible AI in the Enterprise
- The Imperative of Responsible AI
- Hands-On Lab: Experimental Setup
- Building Governance Frameworks for AI
- AI Safety and Guardrail Design
- Regulatory and Governance Landscape
- Sustainable AI at Scale
- Responsible AI Implementation Roadmap
- Future of Responsible AI: Ethical Automation
- Responsible AI Metrics and Performance Indicators
- Key Takeaways
- Reflection Questions
3 Laboratorio en vivo in this lesson — see the labs panel →
05 AI Deployment Strategies for Enterprises 15 topics · 3 Laboratorio en vivo +
- From Prototype to Production
- Enterprise Lifecycle Architecture
- Understanding AI Deployment Patterns
- Hands-On Lab: Experimental Setup
- Model Sourcing and Landing Zone Requirements
- Comparing Deployment Patterns: Pros and Cons
- Business Alignment: ROI / TCO Framework for Deployment Patterns
- Positioning Deployment Patterns Strategically
- Deployment Strategies for AI Applications Powered by LLMs
- Observability, Drift Detection, and Incident Workflow for LLM Deployments
- Performance Optimization in AI Deployment
- FinOps + LLMOps Integration
- Future Trends in AI Deployment
- Key Takeaways
- Reflection Questions
3 Laboratorio en vivo in this lesson — see the labs panel →
06 Prompt Engineering for Enterprises 12 topics · 3 Laboratorio en vivo +
- The Language of Machines
- The Core Principles of Prompt Engineering
- Prompt Engineering in the Enterprise Context
- Hands-On Lab: Experimental Setup
- Single-Input Prompting Scenarios
- Multi-Input Prompting and Scaling
- Scaling Prompt Engineering Across the Enterprise
- Prompt Optimization and Automation
- Ethical and Responsible Prompting
- Future Trends in Prompt Engineering
- Key Takeaways
- Reflection Questions
3 Laboratorio en vivo in this lesson — see the labs panel →
07 Fine-Tuning for Enterprises 11 topics · 2 Laboratorio en vivo +
- Introduction: From General Intelligence to Domain Expertise
- The Concept and Purpose of Fine-Tuning
- The Fine-Tuning Lifecycle
- Fine-Tuning Techniques and Frameworks
- Hands-On Lab: Experimental Setup
- Evaluating Fine-Tuned Models
- Integrating Fine-Tuned Models into Enterprise Systems
- Compliance and Ethical Considerations
- Future Trends in Enterprise Fine-Tuning
- Key Takeaways
- Reflection Questions
2 Laboratorio en vivo in this lesson — see the labs panel →
08 Orchestrating Generative AI Workflows 13 topics · 2 Laboratorio en vivo +
- Introduction: From Models to Systems
- The Concept of AI Orchestration
- Key Objectives:
- Components of an Orchestration Platform
- Orchestration Across Deployment Environments
- Hands-On Lab: Experimental Setup
- Workflow Design and Automation
- Model Orchestration Framework
- Governance and Observability Integration
- Integration with Enterprise Systems
- Future of AI Orchestration
- Key Takeaways
- Reflection Questions
2 Laboratorio en vivo in this lesson — see the labs panel →
09 The Six Ethical Dimensions of Enterprise AI 10 topics · 1 Laboratorio en vivo +
- Introduction: From Compliance to Conscious Design
- The Six Ethical Dimensions of Enterprise AI
- Responsible Infusion: Embedding Ethics into Enterprise DNA
- User-Centric Design and Human Alignment
- Hands-On Lab: Experimental Setup
- Ethical Guardrails and Governance Metrics
- Communication and Cultural Adoption
- Future of Ethical AI in Enterprises
- Key Takeaways
- Reflection Questions
1 Laboratorio en vivo in this lesson — see the labs panel →
10 Designing a Target Operating Model 12 topics · 2 Laboratorio en vivo +
- Introduction: The Shift from Projects to Platforms
- Defining an AI Target Operating Model
- The Seven Layers of the Holistic Operating Model
- Principles Guiding an AI Operating Model
- Feedback Loop and Continuous Improvement
- Hands-On Lab: Experimental Setup
- Organizational Change and Capability Building
- Maturity Roadmap for AI Operating Models
- Challenges in Implementing AI-TOM
- Future of Operating Models in the AI Era
- Key Takeaways
- Reflection Questions
2 Laboratorio en vivo in this lesson — see the labs panel →
11 Cost Optimization Strategies for AI Enterprises 12 topics · 3 Laboratorio en vivo +
- Introduction: The Economics of Generative AI
- Key Levers for Cost Optimization
- Understanding Total Cost of Ownership (TCO)
- The Two Peripheries of AI Cost Optimization
- Hands-On Lab: Experimental Setup
- Balancing Cost, Performance, and Quality
- FinOps and AI-Ops Integration
- Cost-Aware AI Design Principles
- Continuous Cost Optimization and Feedback
- The Future of AI Cost Optimization
- Key Takeaways
- Reflection Questions
3 Laboratorio en vivo in this lesson — see the labs panel →
12 Retrieval-Augmented Generation for Enterprises 14 topics · 2 Laboratorio en vivo +
- Introduction: The Problem of Hallucination
- Understanding Retrieval-Augmented Generation (RAG)
- RAG Architecture for Enterprise AI
- RAG at Scale: Infrastructure and Deployment
- Types of RAG Architectures
- RAG in Enterprise Scenarios
- Hands-On Lab: Experimental Setup
- Measuring RAG Performance
- Integrating RAG into Enterprise Systems
- Governance and Observability in RAG
- Performance Optimization in RAG Systems
- Future of RAG in Enterprises
- Key Takeaways
- Reflection Questions
2 Laboratorio en vivo in this lesson — see the labs panel →
13 Model-as-a-Service (MaaS) for Enterprises 12 topics · 1 Laboratorio en vivo +
- Introduction: From Infrastructure to Intelligence Services
- What is Model-as-a-Service (MaaS)?
- Architecture of Model-as-a-Service
- The MaaS Quadrants: Evaluating Service Models
- Advantages of the MaaS Model
- Risks and Challenges
- MaaS Implementation Framework
- MaaS and AI Ecosystem Integration
- Hands-On Lab: Experimental Setup
- Future of MaaS: Autonomous and Federated Models
- Key Takeaways
- Reflection Questions
1 Laboratorio en vivo in this lesson — see the labs panel →
14 Confidential AI 11 topics · 1 Laboratorio en vivo +
- Introduction: The Trust Imperative in Enterprise AI
- What is Confidential AI?
- Technical Foundations of Confidential AI
- Vulnerabilities in AI Confidentiality
- Confidential AI Architecture for Enterprises
- Confidential AI in Practice: Industry Use Cases
- Hands-On Lab: Experimental Setup
- Governance and Compliance in Confidential AI
- The Future of Confidential AI
- Key Takeaways
- Reflection Questions
1 Laboratorio en vivo in this lesson — see the labs panel →
15 Latency in Generative AI Solutions 8 topics · 1 Laboratorio en vivo +
- Why Latency Matters in Generative AI
- Understanding Latency in Generative AI
- Holistic Latency Optimization Framework
- Balancing Latency, Accuracy, and Cost
- Hands-On Lab: Experimental Setup
- Future of Latency Optimization in Generative AI
- Key Takeaways
- Reflection Questions
1 Laboratorio en vivo in this lesson — see the labs panel →
16 Multi-Modal Multi-Agentic Assistant Framework for Enterprises 12 topics · 1 Laboratorio en vivo +
- The Rise of Multi-Agent Intelligence
- Understanding Multi-Agent Systems in Generative AI
- Hands-On Lab: Experimental Setup
- The Multi-Modal Dimension
- Architecture of Multi-Modal Multi-Agentic Frameworks
- Communication and Coordination Among Agents
- Enterprise Applications of Multi-Agent Frameworks
- Orchestration Tools and Frameworks
- Challenges in Multi-Agent Systems
- The Future: Towards Autonomous Enterprise Ecosystems
- Key Takeaways
- Reflection Questions
1 Laboratorio en vivo in this lesson — see the labs panel →
17 The Future of Enterprise AI 11 topics +
- Introduction: From Automation to Autonomy
- Pillars of the Autonomous Enterprise
- The Architecture of Autonomous AI Systems
- Role of Multi-Agent and Multi-Modal Intelligence
- Ethical Autonomy and Human-AI Co-Governance
- AI-Driven Business Ecosystems
- Future Technologies Driving Enterprise AI Evolution
- The Human Role in an Autonomous AI Future
- Vision 2035: The Autonomous Intelligent Enterprise
- Key Takeaways
- Reflection Questions
18 Appendix 1 topics +
- AI Career Paths Explained | Technical vs Non-Technical AI Jobs
Laboratorios prácticos Our edge
34 Laboratorio en vivos- Identifying High-Impact Enterprise GenAI Use Cases
- Evaluating Risks and Opportunities of GenAI Adoption
- Exploring Enterprise Text Generation Using Hugging Face
- Setting Up a GenAI Development Environment on GCP
- Planning Data Governance and Observability for GenAI Systems
- Designing an LLMOps Strategy for Enterprise Operations
- Selecting Enterprise Models Based on Privacy and Quality
- Evaluating and Benchmarking GenAI Models
- Comparing Enterprise Model Options for Business Scenarios
- Implementing Safety Guardrails for AI Outputs
- Resolving Ethical Dilemmas in Enterprise AI Systems
- Designing Responsible AI Policies for Enterprise Adoption
- Designing Incident Response Workflows for LLM Deployments
- Deploying a Containerized Web Service Using Cloud Run
- Selecting AI Deployment Strategies for Enterprise Applications
- Using Prompt Templates to Standardize Enterprise Interactions
- Scaling Prompt Engineering Across Business Functions
- Refining Enterprise Prompts for Improved Business Outcomes
- Fine-Tuning a Domain-Specific Language Model Using LoRA
- Designing Enterprise AI Workflows
- Building Enterprise AI Workflows Using LangChain
- Designing Cross-Functional AI Workflow Automation
- Measuring Ethical Readiness Using Governance Metrics
- Designing an Enterprise AI TOM
- Developing an Organizational AI Adoption Roadmap
- Designing Cost-Efficient AI Solutions for Enterprises
- Optimizing Token Consumption and Prompt Costs
- Applying FinOps Principles to Enterprise AI Operations
- Selecting the Right RAG Architecture for Enterprise Scenarios
- Building and Evaluating a RAG System Using Retrieval Metrics
- Evaluating MaaS Providers for Enterprise Requirements
- Designing Confidential AI Controls for Sensitive Enterprise Data
- Measuring and Optimizing GenAI Latency
- Building a Multi-Agent Enterprise Assistant Framework
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Preguntas frecuentes
Preguntas antes de empezar
What are the biggest challenges in deploying Generative AI in an enterprise?+
The primary hurdles involve scaling models efficiently, managing significant computational costs, ensuring data privacy and security (Confidential AI), mitigating latency issues in real-time applications, and establishing robust governance for responsible AI. It's rarely a plug-and-play scenario.
How does this course address the 'hallucination' problem in LLMs?+
We dive deep into Retrieval-Augmented Generation (RAG) architectures. You'll learn how RAG integrates external, verified knowledge sources to ground LLM responses, significantly reducing the risk of factual inaccuracies and improving reliability for enterprise use cases.
Is this course suitable for someone without a deep AI research background?+
Absolutely. This course is engineered for practitioners and strategists focused on practical enterprise implementation. While we cover technical foundations, the emphasis is on operationalizing Generative AI, managing its complexities, and understanding real-world trade-offs, not theoretical research.
What are the ethical considerations when implementing Generative AI in an organization?+
Security is woven into every module. From protecting against prompt injections to ensuring PII (Personally Identifiable Information) never reaches an external Large Language Model (LLM), we treat security as a first-class citizen.
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- 1 año de acceso completo
- 34 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito