Kerrie Holley and Bill Brown

Cohort 1:  June 10 - June 12

Traditional machine learning projects often focus on model design and performance. But as you climb the AIscape, the architectural complexity explodes. LLMs, while powerful, are not systems; building scalable, resilient, and valuable AI requires a different approach. This course provides the architectural thinking you need to design and build complex AI systems from ML/DL to Agentic to Compound AI Systems: integrated solutions combining models, cloud infrastructure, data pipelines, and more. Learn to move beyond model-centric thinking and master the art of architecting robust AI systems that deliver lasting business impact.

Elevating AI Architect Skills

This course empowers architects and technical leaders to design and build the next generation of intelligent systems. Moving beyond the excitement of large language models and LLM-powered chatbots, you'll learn to architect cohesive, scalable, and resilient AI Systems, which include AI agents, agentic AI, and the emerging concept of compound AI systems. This course explores the different types of AI systems where their complexity and architecture patterns differ dramatically. Leveraging the AIscape Framework, you'll gain a roadmap for understanding the evolution of AI systems and the architectural patterns that drive them. You'll master proven architectural patterns, decision-making frameworks, and practical considerations for designing modular AI solutions that deliver significant business value and adapt to future technological advancements. You'll also learn to understand which architecture artifacts are essential for the different AI systems. The course explores the AIscape (i.e., AI landscape) framework, which identifies seven types of AI systems, with AGI being identified as a future type, Type 8: Type 0 - Rule-Based / Statistical SystemsType 1 - Traditional ML SystemsType 2 - Deep Learning SystemsType 3 - Generative &amp; Foundational ModelsType 4 - Tool-Using LLM SystemsType 5 - Agentic AI SystemsType 6 - Compound AI SystemsType 7 - Cognitive AI Ecosystems The architecture views required per AI system type will be explored along with the relevant patterns. Beyond system classification, the AIscape Framework serves as a comprehensive skills development roadmap for AI professionals. Whether you're a data scientist looking to advance from Type 1 traditional ML to Type 4 RAG systems, or a software engineer planning progression toward Type 6 compound AI expertise, AIscape provides clear learning paths that align individual skill development with organizational AI evolution. Architectural thinking is the paramount skill being developed to move the discussion away from bias and preferences to systematic architectural reasoning. For example, the active debate on whether to choose between single-agent and multi-agent AI systems represents one of the most important architectural decisions in modern AI Agent deployment, with significant implications for complexity, performance, and maintainability. But there is no universally right or wrong answer; it's an architectural decision that should be grounded in the type of AI system (hence the use of AIscape) being built, along with its business objectives. Rather than calling LLMs a new computing paradigm, we discuss the architectural framing of LLMs as a new software substrate for building language-native applications. They represent a new interface to computing—making it possible to interact with systems via natural language. Your TransformationBy the end of this course, you'll transform from someone who thinks about AI tools and models in isolation to a strategic architect who designs integrated AI ecosystems. You'll gain the confidence to assess any AI initiative using the AIscape Framework, recommend the appropriate architectural patterns, and create the documentation needed to guide successful implementations. Most importantly, you'll develop the architectural mindset that separates strategic technology leaders from tactical implementers—enabling you to shape your organization's AI future rather than simply react to AI trends. Whether you're planning your organization's first AI system or evolving toward sophisticated compound AI solutions, you'll leave with a proven framework, practical patterns, and the architectural thinking skills to build AI systems that scale, adapt, and deliver lasting business value. Additionally, you'll gain a personal AI skills roadmap using AIscape to identify which competencies to develop next in your AI career journey, whether advancing from traditional ML expertise to agentic AI systems or building toward compound AI architecture mastery.

Describe the transformation students will have in your course.

Course syllabus

The course teaches AI fundamentals using the AIscape Framework, making it accessible and valuable to both new and experienced architects.

No Specific AI Expertise Required

The course involves architectural thinking and technical trade-offs, not just high-level business strategy discussions.

Comfort with Technical Concepts

If your events are recurring and at the same time, it might be easiest to use a single line item to communicate your course schedule to students

Tuesday, Wednesday, and Thursday

Feel free to type out dates as your title as a way to communicate information about specific live sessions or other events.

September  17, 2025

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Weekly projects

Course schedule

Solution Architects, CTOs &amp; Designers who need to integrate AI capabilities into enterprise systems and create scalable AI-powered solutions

Software Engineers &amp; Developerswho want to develop architectural thinking skills and use AIscape to plan their career progression in AI.

Technical Leaders &amp; Engineering Managers&nbsp;who need to plan AI skill development, make architectural decisions &amp; and assess team capabilities.

Business Leaders &amp; Product Managers who need to understand AI system types, make investment decisions, and identify resource requirements.

Who is this course for? 

What you’ll get out of this course

Navigate 8 AI system types (ML-Based to Compound AI Systems) to identify which systems serve your business needs and use AIscape as a personal skills roadmap for AI career development.

Master the AIscape Framework for AI System Classification
 

Learn which traditional patterns (microservices, event-driven) and AI-specific patterns (e.g., RAG, agentic workflows, multi-agent) work best for each AIscape level.

Apply Proven Architectural Patterns by System Type

Identify the essential artifacts that drive successful implementations: architecture overviews, decision records, and business artifacts like change management, necessary for a given AI system type.

Create Essential Architecture Artifacts for Success

Transform from reactive technology choices to systematic reasoning that evaluates AI decisions based on business objectives and long-term scalability.

Develop Strategic Architectural Thinking Skills

Understand orchestrating multiple AI components such as LLMs, ML models, AI agents, agentic AI, cloud services, RAG, microservices, APIs into unified, high-performing systems that deliver measurable business value.

Understand Compound AI Systems Architecture

Master architecting AI systems using the AIscape classification framework, patterns, and essential artifacts 

Learn directly from Kerrie Holley and Bill Brown

Kerrie Holley and Bill Brown

Courses

Elevate AI Architect Skills

Lightning Lessons

Elevate AI Architect Skills