Visão Geral

Este curso aborda o projeto, implementação e evolução de arquiteturas corporativas baseadas em Large Language Models (LLMs). O participante aprenderá a construir plataformas empresariais de IA Generativa escaláveis, seguras, resilientes e governadas, integrando modelos proprietários e Open Source, arquiteturas RAG (Retrieval-Augmented Generation), agentes inteligentes, observabilidade, segurança e governança corporativa. O curso apresenta padrões arquiteturais modernos utilizados por grandes organizações para operacionalizar a IA Generativa em escala empresarial.

Objetivo

Após realizar este curso, você será capaz de:

• Compreender os componentes fundamentais de arquiteturas corporativas baseadas em LLMs
• Projetar plataformas empresariais escaláveis para IA Generativa
• Integrar modelos de linguagem, bases de conhecimento e sistemas corporativos
• Implementar arquiteturas seguras, observáveis e governadas para IA em escala
• Avaliar estratégias de implantação envolvendo modelos Open Source e proprietários
• Construir arquiteturas corporativas alinhadas aos requisitos de negócio, conformidade e desempenho

Publico Alvo

• Arquitetos Corporativos
• Arquitetos de Soluções
• Arquitetos de Software
• Engenheiros de IA e Machine Learning
• Líderes de Transformação Digital
• Profissionais responsáveis por plataformas corporativas de IA

Pre-Requisitos

• Conhecimentos de arquitetura de software e sistemas distribuídos
• Familiaridade com Cloud Computing e APIs
• Conhecimentos básicos de IA Generativa e Large Language Models
• Experiência em projetos corporativos de tecnologia é recomendada

Conteúdo Programatico

Module 1: Introduction to Enterprise LLM Architecture

1. Evolution of enterprise AI architectures
2. Large Language Models in the enterprise
3. Business drivers and strategic objectives
4. Enterprise architecture principles
5. LLM platform ecosystems
6. Enterprise AI maturity models

Module 2: Foundations of Large Language Models

1. Transformer architecture review
2. LLM capabilities and limitations
3. Model lifecycle overview
4. Context windows and token management
5. Inference workflows
6. Enterprise use case landscape

Module 3: Enterprise AI Platform Architecture

1. AI platform design principles
2. Shared services architecture
3. AI service layers
4. Multi-tenant architectures
5. Platform engineering concepts
6. Enterprise operating models

Module 4: LLM Integration Architecture

1. API-centric architectures
2. Service orchestration patterns
3. Enterprise integration strategies
4. Event-driven architectures
5. Microservices integration
6. Hybrid AI architectures

Module 5: Model Strategy and Multi-Model Architecture

1. Proprietary versus open-source models
2. Model selection frameworks
3. Multi-model routing
4. Model abstraction layers
5. Fallback strategies
6. Vendor diversification approaches

Module 6: Retrieval-Augmented Generation (RAG) Architecture

1. Enterprise RAG foundations
2. Knowledge ingestion pipelines
3. Vector database architectures
4. Retrieval optimization techniques
5. Context enrichment strategies
6. Enterprise search integration

Module 7: AI Agents and Autonomous Architectures

1. Agent architecture patterns
2. Tool integration frameworks
3. Multi-agent systems
4. Workflow orchestration
5. Autonomous process execution
6. Enterprise automation scenarios

Module 8: Security Architecture for Enterprise LLMs

1. Secure AI architecture principles
2. Identity and access management
3. Data protection and privacy
4. Prompt injection defenses
5. API security controls
6. Zero Trust approaches for AI systems

Module 9: Governance, Risk and Compliance

1. AI governance frameworks
2. Responsible AI principles
3. Regulatory compliance requirements
4. Model governance practices
5. Risk management strategies
6. Auditability and accountability

Module 10: Observability, Reliability and Performance Engineering

1. LLM observability architectures
2. Monitoring and telemetry
3. Performance engineering
4. Scalability patterns
5. Reliability engineering
6. Capacity planning

Module 11: LLMOps and Enterprise Operations

1. LLMOps frameworks
2. Deployment pipelines
3. Model lifecycle management
4. Continuous evaluation processes
5. Cost optimization strategies
6. Operational excellence practices

Module 12: Enterprise LLM Architecture Capstone Project

1. Enterprise architecture design workshop
2. Multi-model platform design
3. RAG architecture implementation planning
4. Security and governance assessment
5. Scalability and resiliency validation
6. Final enterprise LLM architecture project