Anshumani Ruddra

AI Evals for Product Managers

Google

Disney+ Hotstar

Zynga

Product Leader and Super IC at Google

Understand why the PM is the ideal AI evaluation owner. Use empathy and product context to define performance criteria

Own & Lead AI Evaluation as the PM

Move from one user to diverse data, use a 'living' Golden Set for regression, and utilize metrics beyond just accuracy

Build a Robust Generalizable AI Evaluation Framework

Automate evaluation as a continuous process. Use LLMs for qualitative checks and avoid pitfalls

Implement Continuous Evaluation and Avoid Pitfalls

Learn proven approaches for quickly improving AI applications.  Build AI that works better than the competition, regardless of the use-case.

ML Engineer with 20 years of experience

Airbnb

GitHub

DataRobot

AlixPartners

Hamel Husain

ML Engineer with 20 years of experience.

ML Systems & Applied AI Evals Researcher

UC Berkeley

Stanford University

Shreya Shankar

ML Systems Researcher Making AI Evaluation Work in Practice

All lectures are recorded, 8x live office hours.

Cohort 3

Cohort 4

🚨 The next cohort is January 2026. Enroll now and get immediate access to our course reader and community. 🚨All students in this course get:<ul><li>🗄️ Lifetime access to all materials!</li><li>♾️ Unlimited access to future cohorts: never worry about timing or missing out on new material. </li><li>🤖 6 months of unlimited access to our new AI Eval Assistant (more info below).</li><li>🧑‍🏫 10+ hours of office hours to maximize the value of live interaction.</li><li>🏫 A Discord community with continuous access to instructors to get unstuck (even after the course!).</li></ul>---Do you catch yourself asking any of the following questions while building AI applications?1. How do I test applications when the outputs require subjective judgements?2. If I change the prompt, how do I know I'm not breaking something else?3. Where should I focus my engineering efforts? Do I need to test everything?4. What if I have no data or customers, where do I start?5. What metrics should I track? What tools should I use?6. Can I automate testing and evaluation? If so, how do I trust it?If so, this course is for you.This is a flipped classroom setting. All lectures are professionally recorded with an emphasis on live office hours and student interaction.

Eliminate the guesswork of building AI applications with data-driven approaches.

How To Collect Data For Evals

Get immediate clarity and direction with Error Analysis

Implement Effective Evaluations

Master Architecture-Specific Eval Strategies

Run Evals In Production

Ensure That Evals Lead To High ROI

Engineers &amp; PMs building AI products who are interested in moving beyond proof-of-concepts.

Those interested in moving beyond vibe-checks to data driven measurements you can trust, even when outputs are stochastic or subjective.

Founders and leaders who are unsure of the failure modes of their AI applications and where to allocate resources.

Do check the TESTIMONIALS of programs taught by Deepak: https://testimonial.to/deepak-singh/all Learning AI is no longer optional for product managers. Product Management &amp; Products are evolving very quickly. PMs are already being asked to lead AI-driven initiatives—but most don’t yet have the skills or confidence to step up or unsure how to contribute meaningfully. You might be able to get by today. But in a year, not understanding AI will hold you back from key roles, launches, and promotions. This is about future-proofing your role, earning trust, and stepping into the kind of product leadership only a few PMs are ready for. WHY THIS PROGRAM NOW: Most AI courses for PMs are technical or theoretical—this is neither.This program is designed for PMs, by a product leader, with one goal: to make you confident and competent in leading AI products without knowing coding. Here’s what makes it different: - You’ll build a real, portfolio-ready AI product as a capstone project within 12 weeks—without coding- You’ll learn to identify opportunities, scope out, assess feasibility, and manage AI product lifecycle end-to-end- You’ll master how to make trade-offs in performance, cost, and ethics—like real PMs do- You’ll walk away with the frameworks, language, and clarity to lead in rooms where AI is being defined HOW DOES THIS PROGRAM WORK - You can apply for the program by filling in the application.- You will receive the shortlisting status by end of the month or 2 weeks before the start of the program, whichever is nearest. You will get an option to pay and enrol directly.- If you have follow-up queries before enrolment, you will be invited for a session with Deepak.- Once enrolled, participate in weekly 4-hour sessions over 8 weeks with Deepak, every Saturday from 9 am - 1 pm IST. - Real-time doubt resolution through live sessions, and Whatsapp community- Access all the recordings of this course on pmcurve.com in case you missed a session or part of it.- Capstone in the last 4 weeks of the program where you will build an AI Product of your choosing without coding.- Join the community for support and have your questions answered by Deepak and peers within 24 hours. THIS PROGRAM IS NOT FOR YOU IF- You are not in the Product Team or have less than a year of experience in Product- You are looking to just learn the theory and not able to commit to apply the learnings.- You do not understand the tech terms like Architecture, Databases, System Design etc. (Hint: Go read Tech Simplified Book for this)

Learn to lead AI Products with confidence—without knowing coding, 100+ learners

Course syllabus

Hands on experience of working with tech team on building products is essential.

2+ Years of Experience in Product

Be comfortable with tech concepts like APIs, architecture, and system design (reading Tech Simplified is recommended). No coding required.

Have Basic Tech Understanding

You'll need focus, dedicated time, and a builder's mindset to actively apply what you learn—not just passively absorb the theory.

Commitment To Learn

Live classes for 8 weeks for all enrolled learners

Regular Classes - Saturdays

This session would be optional and will be conducted by Deepak for those who plan to do the capstone project.

Project Hours - Sundays

Course Schedule

An experienced PM, SPM, or Product Leader preparing to lead AI initiatives

A founder building AI into your product

Ambitious PM ready to uplevel your career with real, practical AI fluency

Who is this course for

What you’ll get out of this course

Discover and evaluate real AI opportunities using structured frameworks, feasibility checks, and ROI thinking

Translate AI opportunities into actionable product specs, with clear PRDs that reflect business goals and tech constraints

Master the basics of core AI/ML concepts every PM needs—from supervised learning to foundation models and multi-modal interfaces

Design user experiences that embrace uncertainty, build trust, and incorporate user feedback in AI flows

Apply hands-on techniques like prompt engineering, RAG, and model finetuning to shape product outcomes

Learn how to work alongside engineering and data teams across training, deployment, evaluation, and iteration specific to AI Products

Integrate safety, governance, and ethical considerations into your product roadmap—not as afterthoughts, but as design inputs

Launch and scale AI features responsibly, with attention to performance, cost, and long-term maintainability

Experience the true complexity of AI product building—identify gaps, iterate through real decisions, and walk away with the confidence only practice can bring

Add a real AI product to your portfolio—designed, built, and shipped by you (no coding required), applying everything you've learned

Build Production-Ready AI Products in 8 Weeks: AI Product Management, AI Prototyping, Deployment, AI Strategy

Pmcurve

Flipkart

upGrad

Unacademy

Deepak Singh

For PMs with 2-20 yrs experience or Founders. Build production-grade AI product in 8 Weeks in live working sessions, even without a coding background.

Advanced Tech & AI for PMs - Cohort 3

Build trust and proof for your AI product through pre-release testing, in-production monitoring, and analytics.

Head of Applied Scientist, AI Evals

Shopify

Carvana

Harvard University

Arizona State University

Stella Liu

Co-Founder at AI Evals and Analytics

System1

Uptake

Seasalt.ai

UCLA

University of Washington

Amy Chen

Co-founder, AI Evals and Analytics

Cohort 1

Cohort 2

<h2>👉 Why This Matters</h2>In a landscape saturated with AI marketing claims, trust and proof are what set great products apart.If you are building products in healthcare, legal, education, or any other highly specialized industry, you know you cannot rely on “vibe checks.” You need evidence and defensible proof to ensure safety, accuracy, compliance, and reliability.AI systems are inherently probabilistic. What truly differentiates your product is how well you evaluate, monitor, and iteratively improve your AI system.<h2>👉 The Solution</h2>Pre-release evaluations and post-release analytics give you the evidence you need to validate performance, guide iteration, and earn user trust.<h2>👉 About This Course</h2>This is NOT a coding course.It is a methodology and framework course for product and data leaders building AI products that will stand beyond the AI hype.You will apply the battle-tested AI Evals &amp; Analytics Playbook to your own real project, combining:<ul><li>Automated evaluations for scalability</li><li>Human reviews for nuance</li><li>Analytics for real-world reliability</li></ul>By the end of this course, you will have an actionable, organization-ready evaluation and analytics plan tailored to your own AI product.

Build Trust for Your AI Product With Evaluation and Analytics

Walk Away with Execution-ready Playbook

Build Your First AI Evals Team

Execute Comprehensive Pre-launch Testing

Design Experiments for AI products

Monitor Product and Catch Issues Early

Product LeadersBuilding AI products and need a clear playbook for evaluation frameworks, success metrics, and shipping with confidence.

Data LeadersRedefining team scope and structure in the AI era, aligning evaluation, analytics, and accountability.

Seasoned Data ScientistsLeveling up with AI product skills: learn AI evals, LLM-as-a-judge, and design AI-specific performance metrics.

Build job-ready AI prototypes to crack AI PM roles with CVs that pass ATS & recruiter filters by highlighting technical & building skills

AI Lead-built early Agents @Google, Meta

Microsoft

Amazon Web Services

Mahesh Yadav

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

 

Bootcamp 1

After 300+ interviews and helping 100+ PMs break into AI roles in the past 3 years, I can say this confidently: getting into AI PM is harder than ever — not because companies don’t need AI PMs, but because they now expect PMs who can think, evaluate, and build with AI. Most PMs are still stuck at AI 101.What’s not working today: PMs rely on slides instead of shipping with tools like Claude Code or n8n. They talk AI but can’t prototype. They know user flows but not context windows, evals, or how to de-risk ideas before engineering. And because AI shifts every 3–6 months, they can’t tell what’s real vs. hype — so interviews fall flat.This program fixes that.After 10+ cohorts, we’ve distilled everything into a 21-day bootcamp that takes you from “I get AI concepts” to “I can build AI agents, evaluate them, and think like an AI PM.” You’ll vibe-code your own agent, create an AI-ready portfolio, master evaluation-first product thinking, and practice interview loops used by top AI teams.By the end, you won’t just explain AI — you’ll show it with working prototypes, sharper judgment, and the confidence to walk into any AI PM interview like someone who actually builds.

10x your output with tools like Claude Code & n8n and get AI-PM job-ready

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

Build with AI End-to-End: The Templates, Decisions & Systems top AI PMs Use

Turn your AI portfolio into an ATS‑ready CV that recruiters can’t ignore

Ace AI PM interviews with free 1:1 mocks and expert, tailored feedback

Traditional PMs transitioning into AI roles

Professionals from ML/analytics backgrounds trying to reposition for AI-first roles

Engineers or builders pivoting into AI Product