ai-secureops

Threat Modeling AI Applications & Agentic Systems

Abhinav Singh

International Trainer & Speaker, Security Researcher & Consultant.

Explore prompt injections—their mechanics, impact, and why they matter in securing AI systems.

Understanding Common Threat Vectors

Differentiate between risks at the application layer and vulnerabilities within the model itself.

Application Vs Model level threats

Break down agentic systems to reveal where key security gaps and attack surfaces emerge.

Threat Modeling Agentic Systems

🚨🚨Notes:- For current offers/promotions check out the FAQs section below-We follow a flipped-classroom format. All lectures are pre-recorded so folks can go at their own pace, but we’ll still meet 5 times a week for office hours and live sessions. The course includes 35+ hours of live time. Check schedule below- For questions or bulk requests, reach out to: problemfirst.ai@gmail.com- October will be our final cohort. We don’t plan to run this course in 2026, it’s simply gotten too hectic. As much as we love teaching AI, we love building enterprise AI systems a bit more. So if you’ve been considering it, this is your last chance to join.- This course is an independent offering and is not affiliated with, endorsed by, or related to the instructors' current or past employers. ⛳&nbsp;The only prerequisite: you should have coded at least once in your life. The course includes low-code assignments, and even folks who hadn’t touched code in over 15 years have found it approachable and rewarding. That said, a basic understanding of coding really helps you get the most out of it — and of course, there’s AI to assist you along the way. The course is built for everyone, whether you’re a Product Manager, Architect, Director, C-suite leader, or someone seriously exploring agentic AI. Agentic AI or AI systems capable of operating with some degree of autonomy, is transforming how we interact with technology. In the coming years, most software systems will integrate AI agents to enhance their capabilities. This shift will drive a growing demand for professionals who can move beyond surface-level understanding and apply AI effectively to solve real business challenges while navigating practical constraints. This course focuses on practical AI agent development, covering key agentic design and usage paradigms. Instead of just explaining what these techniques are, we focus on when and how to use them, so you're equipped to make informed, business-driven AI decisions. What You'll Learn All core content is pre-recorded so students can focus on two-way interaction. Lectures are watched asynchronously, and we host four office hours each week for questions and brainstorming Week 1 (Let’s get you to understand what problem-first means)Decode why agentic AI breaks traditional software assumptionsFrame hallucinations, latency, and prompt brittleness through the determinism spectrumOpen vs. closed models: tradeoffs across compliance, latency, and costProblem-first, evaluation-driven design using early datasets and proxy metricsDeconstruct a production-grade use case and redesign it across progressive system versions Week 2 (Prompt engineering is still the core part of agents, do it smarter with right evals)Break down the evolution from zero-shot prompts to self-optimizing modelsMaster context engineering: Decomposition, meta-prompts, algorithmic optimizationAnalyze when to use prompting-only systems based on task, cost, and latencyCompare model-level strategies: reasoning vs. regular, and when each makes senseAdd guardrails and evaluation layers using LLM judges, semantic scoring, and offline tests Week 3 (RAG is not dead, it’s in fact the basis of self-improving agents)Address statelessness via dynamic retrieval and memory-backed context injectionBuild robust RAG pipelines with advanced chunking, embedding selection, and retrieval methodsExplore GraphRAG, Agentic RAG and multimodal RAG and other advanced methods and learn tradeoffsArchitect episodic, semantic, procedural, and working memory layers for self-reflective agent behavior Week 4 (MCP from an enterprise lens and multi-agents + Fine-Tuning)Understand planning autonomy in agents and how dynamic tool use and multi-turn reasoning go beyond static workflowsCompare agent levels and their control dimensions: action, planning, evolution, and physical autonomyExplore MCP (Model Context Protocol) and A2A as emerging agent-tool communication standardsInvestigate critical security challenges in MCP and A2A. Understand how guardrails, tool signing, audit trails improve reliabilityAnalyze coordination patterns in multi-agent systems, including shared memory governance, state sync, AI collusion risks, evaluation, logging, and observabilityExplore fine-tuning levers (SFT, RLHF, PEFT etc.), compare with RAG, and determine when to shift from context injection to model adaptation Week 5–6 (Put it all together in a capstone)Work in groups of 6Take a business problem and design/implement a solutionDemo to 4000+ public attendees including leaders, VCs, and hiring managers Homeworks: You'll supplement your learning by building an agentic search system (Perplexity like) in 3 iterations with the final iteration using agentic RAG, MCP and multi-agents. You can choose between low-code/code routes to complete assignments. ---- ❌ Who This Course Is Not For For Those Who Have Already Deployed Gen AI in Enterprise: This course is designed as an applied foundations course for enterprise AI with only basic Python as a prerequisite and no ML background required. If you’re already familiar with deploying AI systems, you won’t gain much from the core content. However, if you're looking to network and refine best practices, you're welcome to join. Those Seeking Heavy Theoretical Knowledge: This course emphasizes applied learning and practical problem-solving, not deep dives into theoretical topics like transformer architecture, pre/post-training optimization, inference techniques, or alignment. Those Who Have Never Coded Before: While we provide low-code options, this course assumes you have some coding experience. It’s not suitable for those who have never written or worked with even basic code. Individuals Expecting Deep AI Research Focus: While we’ll cover cutting-edge techniques, this course is centered on applying AI to business problems, not research-heavy exploration. Scaling and Ops Enthusiasts: This course does not focus heavily on scaling or operational aspects (i.e., LLMOps). Deployment will be covered at a high level, but not in-depth.

Design and build impactful agentic AI systems to solve business problems.

Course syllabus

You should have coded at least once in your life. The course includes low-code assignments, and even folks who hadn

The only prerequisite

We offer generous office hours to support you throughout the course, along with 24-hour help on Slack. Typical weekly schedule:<ul><li>Wednesdays at 10 am PT and Saturdays at 9 am PT: Assignment Support</li><li>Thursdays at 5 pm PT and Saturdays at 8 am PT: Content/Use-case discussions</li></ul>

Live Office Hours

Our weekly community discussion channel covers the latest trends in AI, industry updates, and all the unfiltered tea, open, casual, and freeform.

Chai & AI Sessions

We bring in industry experts to share proven insights and discuss real enterprise challenges

Guest Lectures

Course schedule

Software/AI Engineers, Strategists, Data Professionals, Solution Architects and Consultants who want to master AI system design

Business Leaders and Product Managers seeking to gain the technical understanding needed to make informed decisions &amp; lead AI initiatives

Entrepreneurs looking to understand common generative AI use cases and learn how to develop and implement AI-powered solutions

Who is this course for

What you’ll get out of this course

While most courses stop at teaching tools and frameworks, this course goes further by focusing on solving real-world business problems. You’ll tackle practical constraints like cost, scalability, latency, and performance, learning to design AI solutions tailored to real use cases

Solving Real Enterprise Challenges, Not Just Concepts 

While learning applied AI concepts, we’ll put them into action by building a Perplexity-like AI-powered search system through detailed, hands-on tutorials that demonstrate their practical application (Low code options will be provided)

Apply Concepts to Build an Agentic Search System

Learn how to connect cutting-edge research with real-world applications. For the capstone, you’ll use our curated list of the latest research papers to design and implement solutions for practical business use cases. Some of our capstones have received VC funding too. <a href="https://areganti.notion.site/Capstone-Projects-Problem-First-AI-1e0c91fa32368011b935e00a35d9bceb" rel="noopener noreferrer" target="_blank">Examples</a>

Capstone Project

Gain a deep understanding of key challenges in building AI systems, including handling hallucinations, adversarial attacks, security, privacy issues etc., and learn best practices to evaluate AI solutions comprehensively

Understand Challenges and Effective Evaluation
 

The course includes guest lectures from industry experts, AMA sessions, and our Chai &amp; AI discussions, culminating in a final in-person meetup in the Bay Area. You'll have plenty of opportunities to network and become part of our community.

Access to the Problem-First AI Community

Learn to make decisions tailored to business constraints, understand when & how to apply AI effectively & build a multi-agent application

July 2025 Cohort

Aishwarya Naresh Reganti

Tech Lead | Lecturer | Advisor | Researcher | Speaker | Investor

Kiriti Badam

Member of Technical Staff @ OpenAI | AI Advisor | Ex-Google

Self-paced Cohort

Oct 2025 Cohort

Prompt injection attacks are the #1 security vulnerability in enterprise AI systems. Attackers can manipulate your AI chatbots to steal sensitive data, access internal documents, and bypass your safety guardrails. Most organizations using AI don't even know they're at risk. In 2023, I partnered with OpenAI to run HackAPrompt, the 1st Generative AI Red Teaming Competition ever, to study how the AI models can be hijacked. My research has since been used by teams at OpenAI, Google DeepMind, Meta, and Anthropic to improve their model's resistance to prompt injections. Most notably, OpenAI used it to make their model's up to 46% more secure. Now, I advise Governments &amp; Enterprises how to secure their AI Systems, including teams at OpenAI, Microsoft, Harvard University, Deloitte, Stanford University, and more. And because I love to teach... I created this Masterclass to teach you everything I know about AI Red Teaming! 🛡️ About This Course:This On-Demand Masterclass is the #1 AI Security course for Cybersecurity Professionals, AI Trust &amp; Safety leads, AI Product Managers, and AI Engineers who want to secure their AI systems against real-world threats. You'll gain hands-on experience identifying vulnerabilities in Generative AI systems, including prompt injections, jailbreaks, and adversarial attacks. Using the HackAPrompt playground, you'll practice both attacking and defending AI systems, learning how to break them and how to secure them. This course is practical, not just theoretical. You'll work on real-world projects, analyzing live AI systems for vulnerabilities. These projects prepare you for the AI Red Teaming Certified Professional (AIRTP+) Exam, a 24+ hour assessment that validates your AI security expertise. We've trained 1000's of professionals from Microsoft, Google, Capital One, IBM, ServiceNow, and Walmart. Graduates passed the certification exam and are now AIRTP+ certified, applying their skills to secure AI systems worldwide. 👨‍🏫&nbsp;About Your Instructor:I'm Sander Schulhoff. I'm an award-winning AI researcher and the youngest-ever recipient of the Best Paper Award at EMNLP 2023, selected from over 20,000 submissions. My research on AI security has been cited by OpenAI, Anthropic, DeepMind, Microsoft, Google, and NIST to improve their models. I published the internet's 1st Prompt Engineering Guide two months before ChatGPT launched. Since then, I've partnered with OpenAI on a ChatGPT course and taught over 3 million people how to use Generative AI. Most recently, I led researchers from OpenAI, Microsoft, Google, and Stanford on The Prompt Report, the most comprehensive study on Prompt Engineering to date. 🌟 Learn from the World's Top AI Security Experts:• Pliny the Prompter - The World's Most Famous AI Jailbreaker The most renowned AI Jailbreaker, who has successfully jailbroken every major AI model—including OpenAI's o1, which hasn't even been made public!• Joseph Thacker - Solo Founder &amp; Top Bug Bounty Hunter As a solo founder and security researcher, Joseph has submitted over 1,000 vulnerabilities across HackerOne and Bugcrowd.• Jason Haddix - Former CISO of Ubisoft Bug bounty hunter with over 20-years of experience in cybersecurity as the CISO of Ubisoft and Director of Penetration Testing at HP.• Valen Tagliabue - Winner of HackAPrompt 1.0 An AI researcher specializing in NLP and cognitive science. Part of the winning team in HackAPrompt 1.0.• David Williams-King - AI Researcher under Turing Prize Recipient Research scientist at MILA, researching under Turing Prize winning Yoshua Bengio on his Safe AI For Humanity team.• Leonard Tang - Founder and CEO of Haize Labs NYC-based AI safety startup providing cutting-edge evaluation tools to leading companies like OpenAI and Anthropic.• Richard Lundeen - Microsoft's AI Red Team Lead Principal Software Engineering Lead for Microsoft's AI Red Team and maintainer of Microsoft PyRit.• Johann Rehberger - Founded a Red Team at Microsoft Built Uber's Red Team and discovered attack vectors like ASCII Smuggling. Found Bug Bounties in every major Gen AI model.• Sandy Dunn - CISO with 20+ years of Experience in Healthcare A seasoned CISO with 20+ years of experience in healthcare. Project lead for the OWASP Top 10 Risks for LLM Applications Cybersecurity and Governance.• Donato Capitella - AI Security Blogger and Youtuber With over 12 years of experience in offensive security and security assurance, Donato has gained a following for his AI security work. Alongside his blogs on WithSecure, he has taught over 300k people about building and breaking AI systems on his YouTube channel (@donatocapitella).• Akshat Parikh - Leading Bug Bounty Hunter, AI Security Startup Founder Former AI security researcher at a startup backed by OpenAI and DeepMind researchers. Ranked Top 21 in JP Morgan's Bug Bounty Hall of Fame and Top 250 in Google's Bug Bounty Hall of Fame—all by the age of 16.

Your AI Systems Are Vulnerable... Learn how to Secure Them!

8 Modules covered over 4 Live sessions. Each session is exercise &amp; participation heavy with plenty of time for Q&amp;A with Sander (&amp; Guest Speakers).

Mondays - Live Class Sessions

You'll engage in live prompt hacking sessions that will go over weekly projects by hacking state-of-the-art AIs. These sessions will also introduce students to the latest automatic red-teaming tools like PyRIT and Garak.

Live Prompt Hacking Sessions


This course is hands-on! You’ll work on structured projects that apply red-teaming techniques to real-world scenarios and participate in guided sessions within the HackAPrompt Playground.

Weekly projects

Gain On-Demand Access to the AI/ML Red-Teaming Masterclass &amp; <a href="https://learnprompting.org/courses" rel="noopener noreferrer" target="_blank">Learn Prompting Plus</a>, which includes over 20 hours of courses on ChatGPT (created in partnership with OpenAI), Prompt Engineering, Generative AI, AI Image-Creation, Prompt Hacking, &amp; more.

On-Demand Access to Learn Prompting Plus

Course Schedule

CISOs or other executives aiming to incorporate high-level AI security into their business insights and organizational strategies.

Business or tech professionals transitioning into AI security seeking practical experience, supplementary training, and certifications

Project managers or product owners who want to be more effective at unblocking, supporting, and leading AI-related projects

Cybersecurity professionals seeking to master AI/ML red-teaming techniques and professional development opportunities

Government and regulatory officers responsible for AI policy who want to understand AI vulnerabilities, security, safety, and risks

Who is this course for:

Study real-world AI security breaches to evaluate risks and develop effective prevention strategies. Gain insights into common vulnerabilities and learn how to mitigate future threats.

Analyze Real-World AI Security Breaches

Gain hands-on experience with prompt injections, jailbreaking, and prompt hacking in the&nbsp;HackAPrompt playground. Learn to identify and exploit AI vulnerabilities, enhancing your offensive security skills to a professional level.

Master Advanced AI Red-Teaming Techniques

Learn to implement robust defense strategies against prompt injections and adversarial attacks. Secure AI/ML systems by building resilient models and integrating security measures throughout the AI development lifecycle.

Implement effective defense mechanisms against prompt injections and other adversarial attacks to secure AI/ML systems.

Upon completing the course and passing our AI Red Teaming Professional Certification exam, you'll become AIRTP+ Certified, which validates your expertise, enhances your professional credentials, and positions you as a leader in AI security.

Earn an Industry-Recognized Certification

#1 AI Security Course. Brought to you by Learn Prompting, creators of the Internet's 1st Prompt Engineering Guide.

Cohort 5

Sander Schulhoff

CEO, Learn Prompting (3M+ Learners), HackAPrompt, & Award-winning AI Researcher

Cohort 4

Cohort 6

You Don’t Need to Master Every Tool. You Need to Master the Leadership That Makes Any Tool Deliver Results. New models are dropping weekly, the tech is racing ahead, but the business results aren’t catching up. So if it’s not the tech, what’s really slowing you down? ↳It’s misaligned expectations.↳Shifting direction.↳Burned-out builders.↳Poor decisions being made in the name of urgency. Because, if everyone is a priority, nothing is. Do any of these sound like you? You're leading an AI initiative (maybe as a business, product, or strategy head) and you find yourself wondering: 1) How do I even evaluate whether this AI use case is worth doing?2) Everyone's excited about agents and RAG, but which one fits my workflow?3) My team keeps asking for more budget. How do I make tradeoff decisions across cost, latency, and ROI?4) My execs want results fast, but my builders are blocked. How do I create alignment?5) We’ve launched a few pilots, but nothing’s scaling. Are we missing an operating system? If you’ve asked yourself even one of these questions, then this Agentic AI Business Leadership OS is built for you. Because, as a business leader, you don't need to code, but you do need to lead and deliver results Join the Agentic AI Business Leadership OS by enrolling for this course now!!! 💬 Why I Built This? From my experience leading AI transformations, working closely with leaders across AI startups, enterprise innovation teams, and product orgs, I kept seeing the same pattern: * Smart teams blocked by vague direction* Leaders are under pressure, reacting to hype* Execution breaking down not from tech but from leadership drift This course is the operating system I wish every decision-maker had when they were told to “just bring AI into the business” Because tools change weekly.But execution still runs on systems, tradeoffs, and trust. Why learn from me? If you’ve ever asked, “Why isn’t this AI project moving?” I’ve been the one senior leaders called to diagnose the blockers and rebuild momentum. I’ve worked inside high-stakes C-suites and late-night product reviews, translating strategy into execution on AI projects that couldn’t afford to fail. As an advisor to enterprises, innovation teams, and AI startups, I’ve seen: ↳Roadmaps that excite the board but confuse the team↳Tech that demos well but never ships↳Strategy decks with no system for decisions So I built this course to give leaders not more tools but the operating system to make the right calls. Who This Course Is For? ✅ You’re a business, product, or transformation leader tasked with AI strategy✅ You’ve got domain expertise (telecom, cyber, supply chain or even web3) but feel unsure how to layer in AI✅ You’re responsible for execution, but overwhelmed by vendors, jargon, and pilot fatigue✅ You care more about credible delivery than playing with shiny tools You don’t necessarily need to know the math.But you do need to own the decisions that get AI shipped. What makes this course unique? ✅ It includes real-world case breakdowns, red flag decoders, and tradeoff playbooks✅ It features insights from applied AI leaders and operators in the trenches✅ It is designed for deep transformation (and not vague trends or AI 101s) It’s the OS for how you lead in the age of agentic AI. It didn’t exist, so I built it. 🚀 ❌ Who This Course Is NOT For: 1) Engineers looking for deep technical model training2) AI veterans who've already scaled successful AI-native orgs (though you may benefit from the OS frameworks inside)3) Leaders who want a casual AI overview without execution If you're building in an AI-first world, the pressure to “get it right” is only growing. This course helps you make better decisions, faster. 🚀 Apply now to join the upcoming cohort

Build Scalable Agentic AI Businesses and Lead the AI Economy

Business Leaders &amp; Decision-Makers working closely with technical teams

Entrepreneurs &amp; Founders who are either building an AI-first company or integrating AI agents into an existing business

Product &amp; Strategy Leaders looking for the tools to prioritize AI opportunities, assess feasibility, and measure ROI effectively

Scope the right AI initiatives with business-first thinking

Opportunity Discovery Framework

Make fast, high-quality calls in ambiguous situations, without waiting on perfect data

Decision Velocity Toolkit

A 6-layer leadership model: Opportunity → Economics → Metrics → Flywheel → Org Design → Impact

Agentic OS Stack

Know when to use RAG, agents, open vs. closed models, and why. Spot critical tensions like speed vs. trust, novelty vs. ROI, feasibility vs. ambition and resolve them with clarity

AI Economics & Tradeoff Radar

Design systems with trust, ROI, latency, and feasibility baked in. Learn what “good” looks like, how to measure success, and where most orgs go wrong in tracking AI value

AI ROI & Adoption Metric

Plug AI into real business processes

Workflow Integration

Know where your org stands and how to scale wisely

Agentic AI Maturity Ladder

A systems-first leadership OS for decision velocity, intelligent execution & AI-native transformation

Cohort 1

Vidhi Chugh

Cohort 2

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.

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

Schedule items can also be used to convey commitments outside of specific time slots (like weekly projects or daily office hours).

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? 

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 

Kerrie Holley

Google

Cisco

Walmart

Citi

The Boeing Company

William A Brown

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.

Lightning Lessons

Threat Modeling AI Applications & Agentic Systems