Sriram Natarajan

Fall Cohort

Cohort 1

Are you overwhelmed by AI tutorials and unclear learning paths? Many struggle to navigate the fast-changing AI landscape and find valuable insights. This free 11-minute video reveals the key challenges in building AI applications and why structured learning is crucial. Learn how our course provides a hands-on, practical approach to mastering AI, from development to deployment. Start mastering AI today and stay ahead in your career!

Learn the Key Challenges and How to Build Real-World GenAI Applications

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

AI Engineering for Product Decision Makers

Global Solutions Lead

Learn essential skills with hands-on experience to take an AI idea from concept & design to deployment. Acquire the expertise to excel in AI

Co-founder/CTO AI Startup, 
ex-Googler, 
TEDx Speaker, 
Engineering Coach

AI Engineering Essentials

Sr. Engineering Manager

AI Strategy & Product

AI/ML Gen AI 

IT Director

Many students today are overwhelmed by the sheer volume of AI tutorials and lack a clear learning path. Learners struggle to separate valuable information from fluff and navigate the rapidly changing AI landscape. They also face challenges in integrating AI into existing systems and measuring its real-world value beyond the hype. As AI becomes more integrated into various industries, it's crucial to learn how to leverage these technologies effectively. Investing time in learning AI the right way ensures you stay ahead in your career, gaining the skills needed to build, deploy, and manage AI applications. This course provides a timely opportunity to upskill, offering a structured approach to mastering AI and staying relevant in an evolving job market. A Practical, Hands-On Learning ExperienceThis course is not just about learning AI theory; it's about gaining the practical skills to build and deploy real-world AI applications. Our unique top-down approach starts with an end-to-end view of AI product development, then dives deep into a real-world application that you will prototype and run in a hosted environment. You will understand the different challenges involved in taking your prototype to production, equipping you to move forward beyond the two-day course. 2-day Interactive Sessions•&nbsp;You'll engage in two-day live sessions with peers, enabling interactive learning and real-time feedback. •&nbsp;The course includes hands-on projects where you can apply what you've learned, share your work publicly, and receive personalized feedback. •&nbsp;You'll have opportunities to ask questions and interact with both the instructor and fellow students, fostering a collaborative learning environment. You'll take home:•&nbsp;A blueprint for building production-ready AI applications.•&nbsp;Design templates and a guide for practical use cases.•&nbsp;Access to open-source code and Colab environments for hands-on practice to build your prototype.•&nbsp;An understanding of the evolving AI market from a tech, product, and business perspective. Prerequisites:•&nbsp;Basic understanding of AI applications and some exposure to GenAI tools like ChatGPT.•&nbsp;Familiarity with application development is helpful but not required.•&nbsp;Beginner level Python skills is preferred but not required.

About the Course

Course syllabus

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

Module 1 and Project Work 9 am - 12pm PDT Live Instruction.12pm - 1pm Lunch1pm - 3pm Live Session (continued) 3pm - 5pm Project Work All sessions will be recorded — recordings will be available in your Maven account.

September 21, 2024

Modules 2 and 3 and Continue working on the project. 9 am - 12pm PDT Live Instruction.12pm - 1pm Lunch1pm - 3pm Live Session (continued)3pm - 5pm Project Work All sessions will be recorded — recordings will be available in your Maven account.

September 22, 2024

<ul><li>You will leverage existing Python code and extend it to build your application.</li><li>Gain hands-on experience in deploying your application, getting it up and running, and iterating further.</li><li>You don't need extensive hands-on experience, but some familiarity with Python is helpful.</li></ul>

Hands-on Work

Course schedule

Engineers and Developers aiming for career growth can upskill on the latest AI developments and avoid falling behind.

Tech Leads and Engineering Managers lacking a clear learning path and seeking practical experience to integrate AI into their products.

Tech professionals can cut through AI hype, understand core concepts, and make informed decisions with hands-on, practical learning

Who is this course for

What you’ll get out of this course

<ul><li>Understand the building blocks of an AI application and explore the different choices available. </li><li>Get a step-by-step walkthrough on how to build and deploy an AI application in production.</li></ul>

AI Application Development: From Concept to Deployment

<ul><li>Analyze a real-world AI application by walking through an example of building a Private AI Model for a large retail brand. </li><li>Understand the similarities and differences between multimodal chatbots, copilots, and AI agents. </li><li>Evaluate benefits, assess risks, and improve performance.</li></ul>

Real-World AI Application Analysis

<ul><li>Learn how to successfully evaluate an AI model. </li><li>Dive deep into selecting the right libraries and frameworks for your application. </li><li>Understand production considerations, such as building guardrails and defining success metrics.</li></ul>

AI Model Production Considerations

<ul><li>Explore industry market trends and learn how to integrate AI solutions from a business perspective. </li><li>Understand the product and design challenges involved in deploying AI solutions in production.</li></ul>

AI Industry Deep Dive

<ul><li>Get access to boilerplate code for building your prototype, such as Code Tutor, Website Analyzer, and Calendar/Email Assistant.</li><li>Follow step-by-step instructions to deploy your tuned model and get it up and running.</li><li>Use your model with a simple UI and iterate on it.</li></ul>

Hands-on Exercises

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

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.

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

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.

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

How Product Leaders Evaluate AI Features

Engineering Director, Applied AI @ GEICO | Ex-Google

Cut through vague signals and learn the key engineering factors product leaders must evaluate before signing off.

Understand What Makes an AI Feature Launch-Ready

Apply a clear framework across cost, latency, reasoning depth, fallback design, and infra constraints.

Use a 5-Point Rubric to Spot Risks Before Launch

Get practical prompts and examples to guide model, infra, and integration decisions confidently.

Learn directly from Sriram Natarajan

Sriram Natarajan

Courses

AI Engineering Essentials

AI Engineering for Product Decision Makers

Lightning Lessons

How Product Leaders Evaluate AI Features

Resources

Learn the Key Challenges and How to Build Real-World GenAI Applications