Amplitude AI Analytics

North Star Metrics for AI Agents

Amplitude

Sequoia Capital

Khosla Ventures

Unusual Ventures

Atlassian

Sandhya Hegde

Co-Founder, Calibri Labs, Ex-Amplitude

Justin Bauer

DAU and funnel conversion can't tell you if your AI products are doing quality work.

How to stop tracking workflows, start measuring work.

A composite metric combining user feedback, user actions, and conversation analysis. The hero of your product review.

How to build & monitor Agent Success Rate.

Turn "is this good enough to ship?" from a vibe into a number.

An eval rubric that anchors your release roadmap.

To start leading Agentic AI products, build your own systems, define metrics, scale responsibly and attain a durable competitive advantage

Cohort 9

AI Lead-built early Agents @Google, Meta

Google

Microsoft

Amazon Web Services

Mahesh Yadav

20+ years exp., Ex-Google, Meta Research, AWS AI | 10k+ AI Alums

Cohort 8

Cohort 10

<ul><li>9th cohort (longest running AI cert.) Rated #1 and has most 5 star reviews on Maven </li><li>Mahesh has worked in all AI layers@ Google, Meta and AWS AI </li><li>Mahesh teaches every class on whiteboard and uses real examples from his own experience of shipping agents in production. Self-funding use code "MAY600" to get $600 OFF. Why? AI has affected all products &amp; jobs, making AI skills essential. This course empowers you to dominate in this space starting from zero you will: 1) Get 6-8 live AI PM skills workshops that gives you reall life exposure on what it takes to build AI products. 2) Do labs(hands on) to get in depth understanding of Agentic AI concepts(harness, memory, MCP and RAG). 3) Week on week building and shipping real projects to production. Who?</li><li>Product managers who wish to dominate in Agentic AI space.</li><li>Consultants, Data Scientist, Designers, Program/Project managers who wish to learn AI PM chops and wish to switch to AI PM role ( by doing<s>)</s>.</li><li>Builders(Engineers/ EM's) who wish to become AI Product engineers aka FDE. Sources to check: <ul><li>Explore past projects: <a target="_blank" rel="noopener noreferrer nofollow" href="https://myaicommunity.org/all-projects/cohort8">AI Product Pitch Days</a>,</li><li>&amp; hear directly from alums in our<a target="_blank" rel="noopener noreferrer nofollow" href="https://myaicommunity.org/testimonials"> Wall of Love</a> . Need more : <a target="_blank" rel="noopener noreferrer nofollow" href="https://calendly.com/arjita-legalgraph/30min">BOOK A CALL</a> If in need, check out <a target="_blank" rel="noopener noreferrer nofollow" href="https://docs.google.com/forms/d/e/1FAIpQLSdsZxcoAIgKc81ibL8YT4VuUvLdG8aEqH3zD3mH7gxdAYPFhA/viewform?usp=header">scholarship program.</a> </li></ul></li></ul>

Learn from #1 AI Instructor | Go from Vibes to Production | Year Long Learning

Build a foundation of AI ecosystem, ML Concepts & Agentic AI Landscape

Finding and Translating business needs/opportunities into solvable AI problems

Address AI agent evaluations and deploy with responsible AI tenets 

Create a GenAI/Agentic AI Product Portfolio

Pitch preparation: Story, Metrics, Objection handling

Product Managers who want to pivot into AI or lead AI-native products &amp; those with some AI/ML exposure who want to master agentic systems.

Engineers or tech-leaders wanting to move into product strategy for AI. Those aiming for AI PM roles at top-tier companies &amp; AI startups.

Anyone who wants hands-on build + portfolio + interview readiness, not just theory.

You’ll learn how to design, evaluate, and ship reliable AI systems, becoming a PM who can lead beyond demos.

2026-02

Ex-Google | Prof UCLA & UMN | Founder

Walmart

Gallup

UCLA

University of Minnesota

Hamza Farooq

Founder | Ex-Google |
Adjunct UCLA & UMN, SCU | Venture Partner

AI Product Builder | Startup Advisor

Aishwarya Ashok

2026-01

2026-03

Welcome to the ever evolving top-rated OG course of Maven. 3 years, 15+ cohorts, thousands of students, and a proven record of turning strategy into execution.Once you enroll, you're enrolled for life. Receive all course updates, join any class! <h2>💡 WHAT YOU’LL LEARN </h2><ul><li>Identify high-impact AI use cases with real business value</li><li>Design and evaluate production-ready AI workflows and agents</li><li>Learn orchestration, memory, retrieval, and guardrail patterns used in modern AI systems</li><li>Build the technical intuition needed to lead AI initiatives with confidence </li></ul><h2>🚀 NEW IN THE 2026 CURRICULUM </h2><ul><li>AI evaluation frameworks used in real production systems</li><li>Hands-on Claude Code workshops for building AI agents</li><li>Advanced multi-agent orchestration patterns </li></ul><h2>🤖 THE CAPSTONE EXPERIENCE</h2>You won’t just learn concepts — you’ll build and present a working AI product.Prototype → Workflow → Production Agent<h2> ✨ WHY THIS COURSE?</h2>Most AI products fail for the same reason: they work in demos, not in real workflows.This cohort is designed for people who want to understand how modern AI systems are actually designed, evaluated, and shipped — from orchestration and reliability to real-world production constraints.

Build and Deploy Actual AI Agents that Drive Real Business Impact

Foundations of AI Agents & the GenAI Ecosystem

Thinking Like a PM, Building Like an Engineer

Building Production-Ready Agents

Multi-Agent Systems & Orchestration

Guardrails, Evaluations & Reliability

If you have never written code before, no worries. We’ll give all the support and everything you will need.

Just be willing to learn, we'll take care of the rest

Product Managers&nbsp;looking to integrate AI into their roadmaps and communicate with stakeholders confidently.

Business &amp; Non-Technical Leaders. Executives and consultants who need to translate AI into business value and strategy.

Job Seekers &amp; Career Builders.&nbsp;Professionals aiming to create standout portfolio projects and pivot into AI roles.

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

You leave with deployable systems and documented architecture — not slide decks.

AI & DeepTech Leader & Entrepreneur

Hike

Yatra

Apparel Group

Oracle

Bytelearn

Dr Ankur Narang

Three decades building HPC and AI systems processing billions of transactions

Enterprise AI Agent Architect

Adda247

Mobileum

Kush Khurana

AI product and deep-tech builder, Visiting Professor at Ashoka University

AI Systems Architect & Leader

Stryker

Samsung

Dr Aveek Brahmachari

Stryker Senior Staff Engineer; IIT-ISM, USF PhD; ex-DRDO, Samsung

Founding Cohort

A 4-week intensive for engineers mastering autonomous cognitive workflows — from cost compression and self-correcting loops to revenue-scaling agents that turn AI from a cost center into a profit driver.<a target="_blank" rel="noopener noreferrer nofollow" href="https://deepcorexlabs.com/courses/production-ai-agents">Course Webpage</a>Build The Economic Engine of Agents: This goes beyond the technical deep-dive. We focus exclusively on production-grade impact—engineering the measurable business outcomes that deliver exponential ROI.20x ROI via Cost CompressionAchieve 20x return on investment by reducing operational overhead — replacing manual human-in-the-loop processes with self-correcting agentic loops.100x ROI via Revenue ScalingUnlock up to 100x return on investment by deploying agents that solve high-intent, real-time use cases — turning AI from a cost center into a primary profit driver.Achieve 5–10x lower AI infrastructure costsToken routing, caching, and cost modeling that saves six figures at scale.Systematic evaluation capabilityInternal eval infrastructure — catch regressions before users do.De-risked architecture decisionsBuild-vs-buy calls backed by production data, not vendor pitch decks.Reusable patterns across teamsOne cohort creates blueprints your entire engineering org can adopt.

Deploy Agentic systems that deliver 20x–100x return on investment.

Ship a functioning agent with structured tool use and state management.

Ship a retrieval pipeline that's grounded, verified, and citation-backed.

Ship cost controls and failure recovery into your agent.

Ship your capstone with full eval harness and deployment pipeline.

<h3>Engineering &amp; Product Leaders</h3>Responsible for shipping AI features. Need production patterns beyond prompt engineering and API wrappers.

<h3>Technical Founders &amp; CTOs</h3>Built the prototype yourself. Need it reliable enough for paying customers and investor due diligence.

<h3>Lead Developers</h3>You're the person who makes the AI system actually work — and keeps it working when inputs get messy.

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.

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.

Developing intuition for what makes a great product — evaluating ideas, understanding user needs, and making tradeoff decisions.

North Star Metrics for AI Agents

What you'll learn

Why this topic matters

You'll learn from

Sandhya Hegde

Justin Bauer