GenAI Platform Engineering Course Introduction
This course provides a comprehensive roadmap for enterprise leaders and engineers to design, implement, and manage GenAI platforms. Participants will learn how to accelerate AI adoption within their organizations using DevOps and platform thinking. The course covers key concepts, tools, and techniques necessary for building scalable and secure GenAI platforms.
Participants will gain hands-on experience through interactive workshops and real-world case studies. The course is designed for professionals who are looking to enhance their skills in AI platform engineering and drive innovation within their enterprises.
The course is structured to address the theory, practices, and practical applications of GenAI platform engineering. By the end of the course, participants will be equipped with the knowledge and skills to lead AI initiatives and transform their organizations.
Who Should Attend?
- Enterprise Architects & Leaders looking to integrate GenAI workflows into their existing pipelines.
- Data scientists & MLOps Engineers seeking 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.
