Platform Engineering Strategy Course

This course provides a comprehensive roadmap for enterprise leaders and technical teams aiming to operationalize Generative AI (GenAI) using proven delivery patterns and DevOps best practices. Based on the insights from the Enterprise GenAI Delivery Patterns paper, participants will learn how to navigate critical decision points, address security and governance requirements, and integrate GenAI capabilities into existing DevOps pipelines. We will explore RAG (Retrieval-Augmented Generation), LLM orchestration, platform as a product strategies, and vector database approaches for secure, scalable AI implementations. 

By the end of this course, attendees will have the theory, patterns, and practical blueprints to design GenAI solutions that deliver tangible business value, while minimizing risks around cost, security, trust, and adoption. 

Who Should Attend? 

• Enterprise Architects & DevOps Leaders aiming to integrate GenAI workflows into existing pipelines 

• Data Scientists & MLOps Engineers looking to extend or adapt AI/ML practices with LLM-based systems 

• IT Managers & Platform Teams responsible for centralizing AI services (RAG, model registries, AI guardrails) 

• Security & Compliance Officers overseeing governance, data privacy, and ethical AI standards 

• Senior Technical Stakeholders (CIO, CTO) seeking a strategic understanding of GenAI’s business value 

Prerequisite Knowledge 

• Basic Understanding of AI/ML Concepts: Familiarity with machine learning workflows and terminology (e.g., models, inference, data pipelines) 

• Foundational DevOps Skills: Experience with CI/CD, infrastructure as code, and container orchestration (Kubernetes) 

• General Cloud Computing Knowledge: Comfortable with cloud platforms (AWS, Azure, or similar) 

• Interest in AI Governance & Security: Helpful for engaging with guardrail and compliance discussions