ML Engineer with 10+ years of experience

AI Problem Framing is to AI practitioners what System Design is to software engineers and Product Sense is to PM. This is the foundational thinking skill that separates senior from junior.
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🚨 Pilot cohort: February 16 – March 20, 2026 - limited to 25 students. 🚨
This is the first run of the course, that means:
• Never before published material, you see it first!
• Smaller cohort = more direct access to me
• Pilot pricing won't last
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What you get:
🔄 The Loop, a 5-step framework you'll use to think through every AI project
🧑🏫 Live sessions and office hours where you can bring your real problems
📊 Access to 150+ case studies, the largest collection of AI reframing examples
📋 Production-ready checklists for RAG, Forecasting, GenAI
🎥 Lifetime access to all recordings
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This course is for you if:
• Your AI works in demos but fails in production
• You've spent months on a model only to realize you solved the wrong problem
• Stakeholders ask for solutions that feel wrong, and you can't explain why
• You're leading AI initiatives but came from engineering or product (not ML)
You'll learn from 150+ AI failures in 5 weeks. You gain years of experience and recognize the scars.
How to think through AI problems end-to-end: scoping, debugging, and knowing when to pivot.
Use the Loop: a 5-step framework (Outcome, Deconstruction, Alternatives, Trade-offs, Signals) for your AI solutions
Learn to ask the questions that reveal whether you're solving the right problem.
Recognize the signals that tell you what's actually broken.
Learn whether to fix the data, fix the architecture, or fix the framing, and how to tell the difference.
Shift from executor to AI Architect: question requirements before building them.
Push back with evidence: "I know you want a chatbot, but here's why search is better."
Study real failures so you can spot the warning signs before they become expensive.
Set realistic expectations and catch bad framings before the team spends months on them.
Translate AI trade-offs into business terms stakeholders actually understand.

Enterprise AI veteran: Shipping AI to production, with clarity and humor


Engineers who can build AI but want to master outcome engineering: knowing what to build, not just how
Building RAG, agents, or ML models? Let's get your demos into production! Learn the thinking that makes the difference.
Tech Leads and Engineering Managers who need to evaluate AI/ML proposals and catch bad ideas early
We focus on framing and strategy, not explaining what models or training means. Basic vocabulary lets us go deeper faster.
Real experience gives you context. The frameworks click when you can map them to projects you've actually built or managed.
This is about decision-making, not implementation. You'll learn what to build and why, not how to code it.
9 live sessions • 60 lessons • 11 projects
Feb
17
Feb
19
Feb
23
Feb
26
Live sessions
2 hrs / week
Combination of live sessions covering material and office hours for your questions. Depending on the time zones of the final cohort, I may adjust some of these time to cover as many people as possible.
Tue, Feb 17
7:00 PM—8:00 PM (UTC)
Thu, Feb 19
9:00 PM—10:00 PM (UTC)
Mon, Feb 23
7:00 PM—8:00 PM (UTC)
Projects
1 hr / week
Optional exercises you can do to dig deeper and master the the content
Recorded Lessons
1-2 hrs / week
Lectures are recorded for your convenience

From Mindset through The Loop to Build, Evaluate, and the Pivot decision in one view.
Participation Expectations
This is a practitioner-focused cohort built on trust and discussion.
Participants are expected to engage respectfully and professionally.
Disruptive behavior, bad-faith participation, or misuse of course materials may result in removal.
Content Use Policy
Course content, discussions, slides, and materials are for personal educational use only.
Recording, transcribing, scraping, or redistributing any portion of the course is not permitted.
This includes sharing with employers or third parties.
Violation may result in removal from the course.
$980
USD