AI Product Hub

August 2025

February 2026 (please delete)

Feb 2026

December 2025

Cohort 4

Cohort 1

Cohort 5

Product managers who drive strategy, development, and launch of products.

Measuring and evaluating AI model performance, quality, and reliability for production use cases.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Eliminate uncertainty in shipping AI features

GenAI Product Builder @ Google, ex-Meta

Marily Nika, Ph.D AI/ML

Senior Data, ML and GenAI Scientist

George Zoto

Senior NLP/AI Scientist

AI Evals for PMs Certification

Director Mainstream Innovation Strategy & Business Development 

SVP of Product

Senior Product Manager

Senior Product manager

"All PMs will be AI PMs in the future" This course is designed for product managers who are working with AI, whether you're just entering the space or already embedded in it: identifying the right problems, influencing technical teams, communicating tradeoffs, and building strategies that matter. In AI, most product managers are figuring things out as they go. The tech is fast-moving. The org is vague. The stakeholders have opinions but little clarity. This course won’t teach you how to train a model, but it will show you how to lead in this environment. You’ll leave with frameworks, mental models, and tactical tools for thinking clearly, prioritizing well, and guiding your teams with confidence. It’s based on what actually happens inside companies like Google, Meta, Amazon, and high-growth AI startups, not theory. The course is structured for people with limited time but high standards. This is not a beginner course. It's for people already in PM or technical roles who want to develop better judgment and become the person others trust to lead. The format is live and cohort-based, so you’ll get feedback from instructors and your peers. You’ll build your thinking muscle in real-time and walk away with a playbook you’ll use again and again. Expect candid discussions, clear examples, and rigorous critique. This course teaches practical skills: how to lead with clarity, prioritize under uncertainty, and develop durable product instincts in the face of complexity. Whether you're working with foundation models, building LLM-based features, or supporting infra teams, the same principles apply: sharpen your thinking, build alignment, and lead decisively. Learning outcomes:• Understand the distinct role of a PM in AI product development• Learn how to make prioritization decisions when outcomes are uncertain• Build stronger relationships with ML, data, and research teams• Learn how to avoid common failure modes in AI product work• Gain tools for storytelling, influencing, and aligning execs around an AI strategy• Navigate technical tradeoffs with clarity even if you don’t write code• Apply product thinking to novel, fast-changing problem spaces

Become a top 1% PM at your company in the AI era

Course syllabus

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

Tuesdays & Thursdays

Feel free to type out dates as your title as a way to communicate information about specific live sessions or other events.

May 7, 2022

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

Weekly projects

Course schedule

Experienced PMs: Looking to pivot into AI or currently working with ML teams and seeking to enhance strategic and technical capabilities.

Mid-career AI PMs: Aiming to refine leadership skills, strategic thinking, and gain recognition for their contributions in AI product work.

Founders: Leading AI product efforts and in need of structured decision-making frameworks and strategic guidance.

Aspiring AI PMs: Individuals with a background in software engineering or data science aiming to pivot into AI product management roles.

C-level Executives: Seeking to understand the fundamentals of AI product management to effectively staff and lead AI-focused teams.

Who is this course for

Topics covered

How to reduce noise, recognize patterns, and avoid false certainty when there’s no obvious right answer.

Decision-making under ambiguity

Understand how to validate timing, market readiness, and defensibility for AI initiatives.

The "Why Now" for AI products

Learn the dynamics between model teams and product teams, and how to bridge goals across them.

Working with LLM teams (infra vs applied)

Use language, context, and clarity to get buy-in from both technical and business leadership.

How to earn trust from researchers & execs

Clarify roles in ambiguous teams and avoid stepping on toes while taking ownership.

PM vs TL vs EM roles in AI orgs

Design product strategies that are resilient to model drift, infra shifts, and competitive pressure.

Product strategy: constraints, moats, & goals

Craft compelling, simple stories that help teams and stakeholders understand what you’re doing—and why it matters.

Building narratives that get buy-in

Decide what matters and avoid shipping features just because they're possible.

Leading prioritization without falling into the “build trap”

Lead cross-functionally without getting bogged down in internal debates, confusion, or fear of stepping up.

Handling org politics while staying outcome-focused

A career guide for AI PMs who want to build great products (and get recognized for it).

Dr. Marily Nika, PhD

GenAI Product Lead @ Google, AI Educator @ Fortune, co-founder of AI Product Hub

LinkedIn

Microsoft

Google

Diego Granados

GenAI Product Lead @ Google, co-founder of AI Product Hub

A gentle intro to AI Evals and product quality for AI PMs

“Does it work?”... “Is it good enough?”... “Can we ship it?”...How do you answer these questions for AI products? You’re responsible for “running evals” but what does that mean?How do you choose the right metrics, interpret fuzzy results, and make a confident decision?This course gives you a framework to do just that.<ul><li>Map user value to evaluation (eval) objectives so your metrics aren’t abstract. Define success then translate it into measurable criteria.</li><li>Choose metrics you can actually maintain: capability, safety, UX friction, latency, cost and “does this reduce support tickets or increase activation.”</li><li>Set ship/no-ship thresholds you can defend to leadership.</li><li>Build lightweight workflows that work in real teams: human review where it matters, automation where it lasts, documentation that drives decisions.</li><li>Consider domain constraints (e.g., healthcare safety) and know what to avoid: silent failures, misleading proxy metrics and tests that don’t reflect production.</li><li>Tie everything to ROI: impact vs unit cost, eval coverage vs reliability, and the minimum viable monitoring you need post-launch.</li></ul>Experience AI evals through a case-based approach with a real AI product that we evaluate together.

Acquire and develop a critical skill for product managers who are leading and contributing to AI products.

Make confident ship or hold decisions for AI features

Translate user value into clear evaluation goals

Choose the right metrics for capability, safety, UX, and cost

Set defensible thresholds leadership will trust

Build lightweight evaluation workflows teams can maintain

Navigate domain constraints and avoid common failures

PMs leading AI features, growth, or platform initiatives

PMs who partner with ML teams and want to set evaluation standards

PMs who need to make clear “ship or hold” calls without doing the engineering

Learn directly from AI Product Hub

AI Product Hub

Courses

AI Product Playbook

Workshops

AI Evals for PMs Certification

Lightning Lessons

A gentle intro to AI Evals and product quality for AI PMs