Kyle Lubieniecki

Build an AI Development Stack as a Non-technical Operator

Google

LinkedIn

Coursera

The New York Times

10+ years product leadership. Teaching non-technical operators to build with AI.

Understand the layers—frontend, backend, databases, deployment —and how they work together to create working app

Define what "full-stack" means in modern development

Understand the tool ecosystem and  what these tools do—and which problems they solve in your workflow.

Identify the role of each tool in the AI development stack

Follow a user's journey from button click to database—see how frontend, backend, and dev tools communicate in real-time.

Trace how data flows through your application

Use a simple framework to match your project requirements to the right stack—no overbuilding, no underestimating.

Assess which tools your project actually needs

We show you what's possible with AI, then take you there.

Founder | Meta/Amazon/Statsig/Tencent

Statsig

Amazon

Boston Consulting Group (BCG)

Tencent Games

Yuzheng Sun, PhD

Prev. Evangelist @ Statsig (Acq. by OpenAI), AI Director @ Tencent; Meta/Amazon

Applied Scientist | yage.ai

Yan Wang, PhD

Applied Scientist @ Samsara | Olympic Torchbearer | Ex Pinterest/Microsoft | Ex Tech blogger with 100k+ subscribers

Cohort 11

Cohort 12

Is this you?<ul><li>Chatting endlessly with an “AI intern” instead of designing the context with clear principles.</li><li>Blaming “hallucinations” when the real issue is poor orchestration.</li><li>Trying to run an entire project from a single, overloaded chat window.</li><li>Treating AI like witchcraft and prompt spells instead of engineering.</li></ul>AI gets better every day. But when it’s time to ship something serious, you don’t yet have a reliable way to make AI show up for you.The problem isn’t AI. It’s how you’re using it. This course exists for that exact gap.AI is a new computiung paradigm. To use it well, you have to:<ul><li>Unlearn old patterns</li><li>Install new mindsets and habits</li><li>Build a meta-learning system that can carry your career for the next 20 years</li></ul>That's hard, and we take it seriously. This course is designed to teach you how to fish. On top of lessons and projects, we built two unique tools:<ol><li><a target="_blank" rel="noopener noreferrer nofollow" href="https://youtu.be/dXjcy6XSN_Y">A build platform</a> that consolidates key APIs and lets you deploy your projects with one command—so you can build fast, not fight boilerplate.</li><li><a target="_blank" rel="noopener noreferrer nofollow" href="https://www.superlinear.academy/c/share-your-projects/">A lifetime community</a> where you can share projects, ask questions, and get unstuck long after the course ends.</li></ol>The end of our course is the beginning of your learning, and we will be with you.

Learn AI for the Next 20 Years, Not the Next 20 Days

Engineer Robust Context to Eliminate Hallucinations

Curate "Sterile" Contexts for Peak AI Performance

Architect Multi-Agent Systems to Conquer Complexity

Define Success Criteria to Transmute from Operator to Manager

Build "White-Box" Evaluation Tools to Visualize Quality

Design Self-Healing Loops for Autonomous Optimization

Software Engineers &amp; Developers Ready to build reliable, scalable AI systems integrated into production workflows.

Technical Product Managers &amp; Strategists Seeking the hands-on knowledge to scope, architect, and guide AI initiatives with confidence.

Data Professionals &amp; Automation Experts Who want to transform proof-of-concept scripts into durable applications that multiply productivity.

Transform from consumer of analytics to independent operator - asking sharp questions, validating answers, shipping decisions.

Principal Data Scientist @ Ontra 

Stripe

Nextdoor

PwC India

Ontra

AppFolio

Shane Butler

Principal Data Scientist @ Ontra | Ex-Stripe, Nextdoor, PwC, Appfolio

Data Science Leader |Ex-Microsoft

Microsoft

eBay

Grammarly

Superhuman

Sravya Madipalli

Data Science Leader@Superhuman (Prev. Grammarly)| Ex-Microsoft, eBay, Nextdoor

Head of Data @ Ontra AI | Ex-LinkedIn

Pinterest

Hai Guan

Head of Data @ Ontra | Ex-Nextdoor, LinkedIn, Pinterest, Meta

Cohort 1

The future belongs to full-stack builders, not narrow specialists. As AI reduces the value of deep domain silos, the people who thrive will be the ones who can work across disciplines and ship.You can already prototype designs, write code, and generate content with AI. But analytical independence - the ability to validate your own ideas and make data-informed decisions - remains the missing skill in most builders' toolkits.AI can conduct the analysis, but it can't tell you what to ask or whether the answer makes sense. That requires analytical judgment.This course teaches you to think like a senior Product Data Scientist. You'll learn the frameworks used at FAANG companies to frame problems, choose the right metrics, diagnose root causes, execute the analysis, and turn insights into decisions.-&gt; <a target="_blank" rel="noopener noreferrer nofollow" href="https://join.slack.com/t/dataneighbor/shared_invite/zt-3ox4drc3r-3g0ptSthytkgv0sD6ovvqg">Join AI Builders Slack Community!!</a> &lt;-What makes this different: No SQL. No stats lectures. Five weeks of hands-on real scenarios. You'll build a complete analytical case study - metric specs, deep dives, experiment plans, stakeholder readouts.The result: You become analytically self-sufficient. The last dependency removed from your toolkit. Make data-informed decisions in hours, not weeks.

Ask Better Questions, Make Faster Decisions - with AI as Your Analyst

Frame Problems Like a Senior Data Scientist

Design Metrics That Actually Drive Decisions

Diagnose Root Causes Without Guessing

Design Experiments That Ship

Turn Analysis Into Action With AI as Your Copilot

Synthesize Insights and Prioritize for Impact

Product Managers, engineers, and operators who need analytical independence and frameworks to measure success and drive decisions.

Designers and researchers stepping into strategy roles who need to quantify impact, run experiments, and influence with data-driven insights

Data professionals who want to frame better questions, delegate technical execution to AI, and drive impact with agentic analytics tools.

Build real AI systems from scratch, transforming from an AI learner into a confident full-stack AI engineer with a proven portfolio.

AI Instructor, School of AI

Vivian Aranha

AI educator and builder helping you ship real AI systems, not just learn theory

First cohort building 21 full-stack AI systems from scratch using LLMs, Ollama, and Streamlit.

Alpha Developers

Most people learning AI today are stuck in a frustrating gap. They watch tutorials, experiment with prompts, and understand concepts—but when it comes to building something real, they don’t know where to start. Employers and clients don’t hire for curiosity; they hire for proof. This course exists to close that gap.From Zero to Full-Stack AI Engineer: 21 Hands-On Projects matters because it focuses on what actually moves careers forward: the ability to design, build, and ship working AI systems end to end. Instead of isolated demos, you’ll build complete applications—model serving, backend logic, and user interfaces—exactly the way real AI engineers work.This course solves the critical professional challenge of credibility. By the end, you’re not explaining what AI can do—you’re showing what you have built. You gain confidence, practical intuition, and a portfolio that speaks for you in interviews, client conversations, and leadership discussions.

Stop Learning AI. Start Building Full-Stack AI Systems.

Build End-to-End AI Applications

Work With Local LLMs Like an Engineer

Think in AI Systems, Not Demos

Build a Job-Ready AI Portfolio

Gain Confidence to Build Without Tutorials

Transition From Learner to Full-Stack AI Engineer

You should be comfortable with simple Python scripts, functions, and loops. We focus on building, not teaching Python from scratch.

Basic programming experience (Python preferred)

You’ll run commands to install tools, start models, and launch apps. No advanced system skills required—just basic comfort using a terminal.

Familiarity with command line and installing tools

This is a hands-on course. Progress comes from trying, breaking, fixing, and iterating—not passive watching.

Curiosity to build and experiment with AI systems

<h3>Aspiring AI Engineers &amp; Developers</h3>People learning AI who want real project experience building end-to-end AI applications.

<h3>Software Engineers Transitioning Into AI</h3>Engineers adding AI to their stack by building full-stack AI systems from scratch.

<h3>Builders, Founders &amp; Technical Creators</h3>Builders who want to ship AI-powered products and build a strong, credible portfolio.

Build, deploy, and scale real AI systems—transforming you from learner to job-ready AI Engineer.

Alpha

Beta

Charlie

Train Models. Build Systems. Deploy AI

AI educator and engineer who builds and teaches production-ready AI systems.

Delta

Many people learn AI by watching tutorials or training models in isolation—but struggle when asked to build, evaluate, deploy, and maintain real AI systems. This course exists to close that gap.The critical challenge today isn’t learning algorithms—it’s turning AI knowledge into production-ready systems. Engineers often know Python or machine learning concepts but lack confidence in data pipelines, model evaluation, deep learning frameworks, and deployment workflows. As a result, projects stay in notebooks and portfolios don’t reflect real-world skills.This bootcamp is designed with empathy for that struggle. It takes you step by step from Python fundamentals to advanced deep learning and MLOps, always anchored in hands-on projects that mirror real engineering work. You won’t just train models—you’ll engineer complete AI solutions, tune them, deploy them, and understand how they behave in production.By the end, you don’t just “know AI.” You can build, explain, and ship AI systems with confidence—the exact skill employers and clients are looking for today.

Learn AI Engineering by Building Real Systems End-to-End

Build end-to-end AI systems

Master core machine learning techniques

Engineer features that improve model performance

Develop deep learning models confidently

Deploy and operationalize AI models

Think and work like a professional AI Engineer

Engineers and developers who want to transition into real-world AI engineering roles.

Data scientists seeking stronger system-building, deployment, and MLOps skills.

Students and professionals building an AI portfolio for jobs, freelancing, or startups.