Amplitude AI Analytics

AI Feature Analytics: A Practical Framework

Human37

Deloitte

Semetis

Glenn Vanderlinden

Co-founder, Humans37 | Data Strategy & Operations Expert

Apply a decision-based model that tracks how users engage with AI features, not just whether the feature fired.

Measure AI feature behavior

Use Glenn's decision map to link every instrumented event to a specific question your team can answer and act on.

Connect events to business questions

Apply a property design pattern that keeps your AI tracking clean and consistent as you ship more features.

Design a scalable AI event taxonomy

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

LinkedIn

Pinterest

Hai Guan

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

Cohort 1

Cohort 2

Cohort 1 is in Session, but it's not too late to enroll!! Kick off was April 20. Final registration closes April 27. Everything has been recorded so you don't miss a thing! As AI reduces the value of deep domain silos, the people who thrive will be the ones who can work across disciplines and ship. 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 end-to-end, 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.

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

GenAI Product Builder @ Google, ex-Meta

Marily Nika, Ph.D AI/ML

Product Manager AI&ML @ Google

Diego Granados

Senior Data, ML and GenAI Scientist

George Zoto

Senior NLP/AI Scientist

Cohort 4

Cohort 5

“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.

Eliminate uncertainty in shipping AI features

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

Master the most powerful decision making protocols through proven tools, practical frameworks, and real-world applications

AI Decision Coach & Product Leader

Vivek Juneja

20 years of leading teams, building products & managing large projects at scale

Feb 2026

June cohort

AI tools can now draft your emails, summarize your data, and generate recommendations in seconds. But the decisions that shape your career, your team, and your organization still depend on how you frame the problem. Most leaders use AI to move faster. The best leaders use it to think more clearly. In this course, you will learn a practical method for framing high-stakes decisions and practice using AI as a thinking partner, not a decision-maker. You will work with the Winning Frame methodology, use AI in three specific roles (Mirror, Challenger, and Reframing Generator), and walk away with a repeatable process you can apply to real decisions at work and in life.

Make better decisions with AI, not despite it

Learn the Winning Frame method for reframing decisions before AI runs with them

Leaders and managers adopting AI tools who want to make better decisions, not just faster ones

Senior ICs and founders facing ambiguous, high-stakes choices where AI outputs are helpful but not sufficient

Anyone who has caught themselves accepting an AI recommendation without questioning the frame it was built on

Shift from ad hoc AI efforts to disciplined, structured, workflow-driven collaboration before significant investment.

Workshop Lead: Netflix, Google, LinkedIn

Google

Netflix

Paul Bryan

I help organizations design and develop AI products that deliver business value.

July 2026 Cohort

<blockquote>AI applied to the wrong workflows leads to chaos. This course helps Enterprise teams launch successful AI pilots by aligning on workflows, roles &amp; responsibilities, risks, guardrails, and AI functional requirements before major investments.Industry reports suggest that over half of enterprise AI pilots never scale to production. Many pilots stall because the workflows were not ready for AI.This course addresses the real bottleneck: cross-functional alignment on key details before committing to technical debt.You will leave with:<ul><li>An AI Workflow Canvas: A structured way to identify where AI can move the needle in your organization</li><li>An AI Workflow Scorecard: A shared evaluation framework your cross-functional team can use to assess value, feasibility, data readiness, and risk</li><li>A Thin-Slice Prototype: Storyboards, scene cards, and an action flow diagram that aligns stakeholders across PM, DS, UX, Eng, and Biz on exactly what the AI system will do, feasibility and risk, and where humans are needed in the loop.</li></ul></blockquote>

Identify High-ROI Workflows Ready for AI

Identify high-friction workflows suitable for Enterprise AI

Prioritize AI initiatives using structured evaluation criteria

Clarify roles and decision rights before pilot launch

Surface feasibility and governance risks early

Make AI behavior visible to cross-functional stakeholders

Define readiness criteria before committing to a pilot

AI product team members (PM, UX, DS, ENG) who need a shared framework to align on workflows, feasibility, and responsibility before building

Product and business leaders accountable for Enterprise AI outcomes who must prioritize workflows and reduce risk before committing budget

AI governance, legal, and risk stakeholders who need structured ways to surface constraints, guardrails, &amp; ownership early in AI initiatives

The Agent-Powered Super IC

Agentic AI

Coding with AI

AI Workflows

Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product Sense

Product Discovery

User Research

Prototyping

Growth

Analytics

Tech Foundations

Strategy

Influence

Leadership

Career Growth

Machine Learning

LLM Ops

Context Eng

Security

System Design

Figma

Design Systems

Visual Design

Design Strategy

Automation

Content Marketing

Demand Gen

Go-to-Market

Product Marketing

Positioning

Social Media

Brand

B2B Marketing

SEO & AEO

AI for Leaders

AI Governance

Communication

Management

People Operations

Exec Presence

Storytelling

Goal-setting

Personal Brand

Fundraising

Everyone

Operators

Data Scientists

Business Analysts

User Researchers

Customer Success

Project Managers

HR Professionals

Sales People

Lawyers

Finance

Investors

Real Estate

Educators

Creators

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

Engineers and engineering leaders who build, deploy, and scale software systems.

Data scientists who apply statistical and ML techniques to extract insights from data.

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

Using data and metrics to measure performance, run experiments, and inform decisions.

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

AI Feature Analytics: A Practical Framework

What you'll learn

Why this topic matters

You'll learn from

Glenn Vanderlinden