AI for Architects is a three-day intensive program designed for architects and technical leaders responsible for building and operating enterprise-grade AI systems. The course focuses on the architectural patterns, platforms, and operational practices required to move from experimentation to production.
Participants will explore modern AI system design, including retrieval-augmented generation (RAG), agentic AI, and multi-agent orchestration. The course examines emerging standards such as the Model Context Protocol (MCP). It also covers practical approaches to tool integration, context management, and AI system coordination.
Beyond design, the course emphasizes production readiness—covering CI/CD for AI, evaluation frameworks, observability, cost control, and governance. Attendees will gain a clear understanding of how to build AI systems that are scalable, reliable, and aligned with enterprise requirements.
By the end of the course, participants will be able to design AI architectures that balance innovation with operational discipline, enabling sustainable and secure adoption of AI across the organization.
Who Should Attend
Software, System and Solution Architects, Tech Leadership and Senior AI staff
What Attendees Will Learn
Upon completing this course, participants will be able to:
- Understand the end-to-end AI system lifecycle, including data pipelines, model development, evaluation, deployment, and continuous improvement
- Evaluate architectural trade-offs across model selection, training vs. inference, latency, scalability, and cost optimization
- Design and implement agentic AI systems, including tool use, memory, context management, and multi-agent collaboration
- Apply modern platform engineering practices (DevOps, CI/CD, GitOps) to AI and agent-based workloads
- Architect AI systems that integrate with enterprise platforms, APIs, and data ecosystems
- Implement observability, evaluation, and feedback loops to monitor model and agent behavior in production
- Design for security, privacy, and governance, including responsible AI and regulatory considerations
- Plan for Day 2 operations, including lifecycle management, versioning, retraining, and system evolution
Prerequisites
- Experience with software or systems architecture
- Familiarity with development and operational environments such as Linux, Git, containers, and CI/CD pipelines
- Basic awareness of AI/ML concepts is helpful but not required