Cohort 1

Master critical frameworks to select models, integrate AI, design scalable products, and guide teams to launch production-ready features.

Sriram Natarajan

AI Engineering for Product Decision Makers

Google

GEICO

TEDx

Stanford Baseball

UMass Amherst

Today’s PMs and Engineering Decision Makers are under pressure to "build with AI" — but without the right frameworks, costly mistakes happen fast:&gt;&gt; Product Managers are expected to launch scalable AI systems and lead roadmaps, yet often lack the frameworks to evaluate models, validate builds, and confidently sign off on launches.&gt;&gt; Engineering Leaders are tasked with selecting models and designing architectures without fully grasping long-term tradeoffs, leading to slowdowns, spiraling costs, and technical debt.Without a structured evaluation process, teams lose velocity, waste resources, and build features that fail to scale in production. 🛠️ What you’ll build and applyIn this course, you’ll work live with me to develop a practical, step-by-step AI product decision framework. You’ll apply each concept immediately to real-world use cases and leave ready to guide product and architecture decisions with confidence.You’ll:* Walk through real-world product goals and map them to the right model choices — task-specific models, LLMs, or agents.* Apply a five-point evaluation framework (cost, reasoning depth, latency, UX risks, cloud implications) to guide model selection.* Make live architecture decisions — when to use APIs, RAG, fine-tuning, or RLHF — and understand the tradeoffs you're committing to at launch.* Design smarter UX flows based on real model limitations like hallucination risks and context window constraints.* Pressure-test readiness — by building launch qualification plans, fallback designs, and evaluation checkpoints.Each exercise ties directly into building your own AI feature decision plan, which you’ll submit for personalized feedback after the course. 🚀 Key TakeawaysThis is a breadth-first, system-wide view of AI product delivery, with deep dives into critical decision points that define success or failure.You’ll:→ Apply the 5-layer AI stack (UX → App → Model → Cloud → Infra) to real-world product builds. → Build your own decision rubric for model and system selection. → Evaluate architecture tradeoffs using the AI Product Decision Matrix. → Create a reviewed, production-ready AI roadmap balancing business goals and technical constraints. → Pressure-test your launch plan using real-world evaluation and risk planning. After this course, you’ll confidently:✔️ Pick the right model and integration path based on your product’s needs — not hype.✔️ Evaluate if your AI feature is production-ready — before wasting resources.✔️ Lead engineering, UX, and vendor conversations with technical clarity.✔️ Align executives and teams around a defensible, scalable AI roadmap.✔️ Avoid costly rework by asking the right technical questions early.This isn’t a course where you "consume information" — it’s a course where you build real decision frameworks alongside me, apply them live, and walk away ready to lead. 💬 What past learners are saying:"Nice deep dive from Sriram, love the delivery and how all the topics were articulated!!!" - AI/ML Lead, Amazon "This is such a great course! It unveiled the magic behind the scenes and grounded the overall view. So valuable for product managers or any role really to have this architect literacy! I appreciate you Sriram for your valuable time &amp; effort!!" — Group Product Manager, CompanyCam "This course is an excellent choice for anyone looking to get a well-rounded understanding of AI. It demystifies the AI hype, providing a clear and structured overview of the subject. The course covers a wide range of tools and platforms available for AI development. The hands-on projects were a great way to apply the concepts in real-world scenarios. I highly recommend it for professionals, especially engineers and engineering managers, aiming to deepen their AI knowledge and quickly get up to speed with practical applications." - Sr. Engineering Manager, Chamberlain Group

Master decision-making frameworks to launch AI features with confidence

Product Managers leading or joining GenAI efforts who want to make better calls on models, architecture, and production readiness.

Engineering Managers and Tech Leads expected to deliver GenAI features, but lacking clear frameworks for vendor, model, and system choices.

AI Decision Makers looking to align teams, vendors, and roadmaps and avoid costly rework or hype-driven distractions.

Who is this course for

What you’ll get out of this course

Walk through real product goals and model options, and practice real-time model selection based on business and system tradeoffs.<ul><li>Deep dive into how agents and copilots are architected.</li><li>Assess model capabilities, latency, and cost for MVP and scaling.</li></ul>

Apply a five-point framework to pick the right AI model for your use case.

Make smart system integration choices by applying real-world architecture tradeoffs.<ul><li>Practice choosing between APIs, RAG, fine-tuning, or reinforcement learning using the AI Decision Matrix and </li><li>Understand the operational risks you're signing up for.</li></ul>

Architecture Decision-Making: APIs, RAG, Fine-tuning, or RLHF

Build a production qualification plan that identifies risks before launch.<ul><li>Design model evaluation criteria and readiness reviews tied to real-world examples.</li><li>Build fallback strategies and stress testing plans to pressure-test AI product launches.</li></ul>

Production Readiness and Launch Qualification

Design user experiences grounded in model behavior and technical constraints.<ul><li>Design UX flows to account for model limitations like hallucinations and context window constraints.</li><li>Apply grounding strategies to improve reliability, reduce risks, and enhance user experience.</li></ul>

UX Strategy for AI Applications

Create a launch-ready AI product plan and defend it with technical fluency.<ul><li>Bring together model choices, system architecture, and UX strategy into a complete AI feature roadmap </li><li>Get personalized instructor feedback to refine it.</li></ul>

AI Product Roadmap Building and Stakeholder Alignment

Provide a brief description for each module as a way to sell the value of what students will be learning throughout the course.

First Module

Second Module

Third Module

Course syllabus

Sr. Engineering Manager
Chamberlain Group

Director, AI Strategy & Product Consultant, ASAI Services

Learners feedback from a slightly modified version of this course.

Meet your instructor

This will be a 2-day interactive and intense live session that covers key concepts and applies them to your projects. You are expected to be present for the entire duration to fully utilize the material and takeaways.

Saturday & Sunday

9 am - 12 pm PDT - Live Session.1 pm - 2:30 pm PDT - Case Study Walkthrough and Project Work All sessions will be recorded — recordings will be available in your Maven account

June 21-22, 2025

This is a live, 2-day intensive, followed by one week of async support:<ul><li>🔍 Case studies of real-world GenAI launches</li><li>⚒️ Design challenges: RAG vs fine-tuning vs APIs</li><li>🧠 Deep dives: How agents, copilots, actually work</li><li>🗂️ Reusable templates to apply immediately in your job</li></ul>

Weekly projects

Course schedule

This course builds on live workshops and hands-on projects

Active hands-on learning

You’ll be interacting with other learners through breakout rooms and project teams

Interactive and project-based

Join a community of like-minded people who want to learn and grow alongside you

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