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

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 9

<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/d/cx4w-4n5-32n/1-1-with-team-mahesh-yadav">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.

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

Build your first AI eval in 3 hours — on your own use case, with your own data.

Founder @Lovelaice | 10 years in Product

Finastra

Trading Central

Printify

Madalina Turlea

Product leader with 10+ years in platform and SaaS products | Founder Lovelaice

Founder @ Lovelaice | 2x founder & CTO

Teleclinic

Nilo

SIXT Group

Freeletics

Catalina Turlea

2x founder & CTO | 14+ years in tech | Founder @Lovelaice

April

<blockquote>Whether you're building your first AI feature or exploring what AI could do in your product, there's a moment every PM hits: you look at the AI output and think "this seems okay?" but you don't have a structured way to know for sure.You've heard about evals. You know vibe checking isn't enough. But where do you actually start?This workshop takes you from that starting point to a working evaluation framework, built on your use case, with your data, in 3 hours.You'll write a prompt, run it across multiple LLMs, read and annotate real AI responses to understand where they break, find failure patterns through error analysis, define automatic metrics based on what you found, and measure whether your improvements actually worked.You'll leave with your own accuracy metric, a failure taxonomy for your use case, and a repeatable process to evaluate and improve any AI feature from here.Every participant works hands-on on their own use case. We've run 1,000+ AI experiments with product teams and this workshop is the compressed version of what we've seen work.</blockquote>

Design, build, validate and iterate on an AI feature, hands-on

Understand the most important components of building with AI at scale

Build an evaluation framework to scale your feature

Learn how to identify failure patterns. Then iterate on both prompt and evals

The PM who owns an AI feature but not the process around it

The PM about to pitch an AI feature internally

The product lead responsible for a whole team's AI quality

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.