AI Essentials for Engineers
Transform your engineering workflows with hands-on AI: Deploy LLMs, automate infrastructure, and master the latest tools and protocols for secure, compliant, and efficient operations.
What you will learn in this course
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.
audience
This course is designed for
- Software engineers
- DevOps professionals
- AI/ML practitioners
prerequisites
To get most out of this course, you should have:
- Basic programming knowledge (Python recommended)
- Familiarity with cloud platforms and version control (Git)
style
Our trainers have years of experience and will deliver the right mix of:
- Interactive lectures
- Hands-on labs with guided exercises
- Real-world use case discussions
- Group activities and Q&A sessions
Technical requirements
We recommend the following equipment:
- Stable internet connection
- Access to AI tools and services (e.g., OpenRouter, Groq, OpenAI API)
- Browser compatible with modern web applications
- If you're eager to try out demonstrated tools on your own: Pre-installed code editor (e.g., VS Code) with sufficient hardware and a Python installation
Agile Testing Essentials with Ruby
Hands-on exercises will give you the expertise you need to automate your tests with Cucumber and Ruby.
Details & booking