AI Essentials for Engineers

Agenda

AI Engineering Landscape & Compliance Framework: Modern AI/MLOps trends, EU AI Act implications for engineering teams, responsible AI deployment, and risk assessment frameworks for enterprise applications.

Local LLM Infrastructure & Hosting Strategies: Architectural decisions for local vs. cloud deployment, hardware considerations, user interfaces, and cost optimization strategies for LLM operations.

Advanced Knowledge Retrieval Systems: Evolution of RAG, Cache-Augmented Generation (CAG) patterns, vector databases, BYOLLM and Fine Tuning, and performance optimization techniques.

Model Context Protocol (MCP) Implementation: MCP architecture deep dive, server and client development patterns, custom tool integration and enterprise system connectivity.

AI-Enhanced Infrastructure Automation: Infrastructure as Code with AI assistance, Terraform AI, Pulumi AI, intelligent monitoring and observability, predictive scaling, and cost optimization through AI analytics.

AI Security & Compliance Operations: AI system vulnerabilities and mitigations, prompt injection defenses, Mondoo security scanning integration, and privacy-preserving techniques.

Autonomous Workflow Automation & AI Agents: Agentic AI architecture patterns, visual automation tools, multi-agent coordination, and business process integration.

Production AI Systems & Future Trends: CI/CD pipeline optimization, intelligent testing frameworks, edge AI deployment, multi-cloud orchestration, and emerging technology roadmap.

Hands-On Labs: Comprehensive practical exercises including complete local LLM setup, custom MCP server development, AI-enhanced infrastructure management, security assessment implementation, visual workflow automation, and end-to-end AI engineering pipeline deployment.