AI Product Management Masterclass
Build, Lead, and Ship AI Products with Confidence - even if you're not technical.
Complete Curriculum
Master ML, Deep Learning, and GenAI from a product-first lens.
Module 1: Classical Machine Learning for PMs
Supervised vs. unsupervised learning, core models (Logistic Regression, Decision Trees, XGBoost), ML lifecycle, evaluation metrics (precision, recall, F1, AUC), real-world use cases (churn, fraud, personalization).
Module 2: Deep Learning Foundations
Neural networks, CNNs for vision, RNNs/LSTMs for sequences, limitations (data hunger, cost, explainability).
Module 3: GenAI & Transformer Architecture
How Transformers work (attention, decoder stacks), tokenization, logits, temperature, sampling. LLMs: GPT, Claude, Gemini, LLaMA, Mistral. Prompt engineering, OpenAI vs. open-source, fine-tuning vs. RAG vs. adapters.
Module 5: Prompts and Prompt Engineering
Prompt basics, prompt templates, prompt chaining, prompt evaluation, prompt best practices for LLMs and GenAI products.
Module 6: AI Agents
Introduction to AI agents, agent frameworks, multi-agent collaboration patterns, use cases, and product implications.
Module 7: Model Context Protocol
Understanding the Model Context Protocol (MCP): how context is managed and passed to models, best practices for context window management, and implications for product design and user experience.
Module 8: Infrastructure & Cost Fundamentals
Inference vs. training cost, GPUs, cold starts, caching, serverless deployment, token pricing, latency impact.
Lead cross-functional teams to build AI that works, ethically and effectively.
Module 1: What Makes AI PM Different?
Deterministic vs. probabilistic products, non-linear iterations and fuzzy MVPs, AI features vs. traditional features.
Module 2: The AI Product Lifecycle
Framing AI problems (prediction, classification, generation), data availability → modeling → feedback loop, shipping v1 with uncertainty, success metrics: product vs. model KPIs.
Module 3: Collaborating with ML/GenAI Teams
Role clarity: PM vs. data scientist vs. ML engineer, writing AI-ready PRDs, prompt iteration and QA workflows, working with model uncertainty.
Module 4: AI Product Requirements
Defining clear and actionable requirements for AI products. Translating business objectives into technical specifications. Managing evolving requirements in iterative AI development. Best practices for documenting and communicating AI product requirements.
Module 5: Responsible AI & Risk
Bias, fairness, explainability. Regulatory (GDPR, CCPA, OpenAI usage policies). Mitigating hallucination and abuse. Ethical UX: disclaimers, confidence scoring, user control.
Module 6: AI Product Design
Designing intuitive AI experiences, user research for AI products, prototyping AI features, and managing user expectations with probabilistic outputs.
Module 7: AI Product Metrics
Evaluation metrics (for PMs): precision, recall, latency, coverage. LLM-specific: helpfulness, prompt sensitivity, token usage. Monitoring in production: drift, feedback loops, retraining signals.
Apply what you've learned to build a real, working AI product with direct instructor guidance.
Module 1: Scoping & Planning
Choose a solvable AI problem (classification? generation?), define your user, workflow, and data needs, write a lightweight AI PRD (problem, data, model, UX).
Module 2: Dataset & Model Strategy
Source your dataset (Kaggle, scraping, simulation). Choose model approach: ML (scikit-learn) or GenAI (OpenAI API or OSS model). Prototyping in notebooks, LangChain, or Hugging Face.
Module 3: UX, Prompts & Feedback
AI UX patterns: confidence, retries, user override. Prompt design: system vs. user prompts. Real-time feedback loops: thumbs up/down, rating prompts. Deploy via Streamlit or Vercel.
Module 4: Monitoring & Iteration
Add observability: latency, helpfulness, usage. Model versioning, prompt A/B testing. Drift detection and prompt retraining.
Showcase your mastery by building and presenting your own AI product from 0 to 1.
Project Part 1: Project Ideation & Scoping
Define a solvable AI problem, identify your target user and their workflow, and outline initial data needs. Craft a lightweight AI Product Requirements Document (PRD) covering problem, data, model, and user experience.
Project Part 2: Data & Model Prototyping
Source or simulate your dataset. Choose an appropriate model approach (e.g., scikit-learn for ML, OpenAI API or open-source models for GenAI). Begin prototyping in notebooks, LangChain, or Hugging Face environments.
Project Part 3: UX & Feedback Loop Design
Design intuitive AI UX patterns, including confidence indicators, retry mechanisms, and user override options. Develop effective prompt designs (system vs. user prompts) and implement real-time feedback loops like thumbs up/down or rating prompts.
Project Part 4: Deployment & Monitoring
Deploy your AI product prototype using platforms like Streamlit or Vercel. Set up basic observability for key metrics such as latency, helpfulness, and usage. Implement initial model versioning and prompt A/B testing strategies.
Project Part 5: Iteration & Presentation
Iterate on your product based on user feedback and monitoring signals. Prepare a comprehensive demo walkthrough of your AI product. Write a project reflection or mini case study, and optionally record a 60-second product pitch to showcase your work.
Leverage your new skills to advance your career in AI product management or launch your own venture.
Module 1: Navigating the AI PM Job Market
Crafting an AI PM resume, interview strategies for AI product roles, networking in the AI ecosystem, and identifying key companies hiring AI PMs.
Module 2: Launching Your AI Startup
Ideation to MVP for AI startups, securing early funding, building a founding team, and scaling your AI venture.
Accelerate your career transition with personalized guidance from an industry expert.
Session 1: Career Path & Skill Gap Analysis
A 30-minute personalized session to assess your current skills, identify gaps, and map out a clear path to becoming a successful AI Product Manager.
Session 2: Resume & Interview Strategy
A 30-minute deep dive into optimizing your resume for AI PM roles and developing winning interview strategies, including behavioral and technical questions.
Session 3: Networking & Job Search Tactics
A 30-minute session focused on effective networking in the AI ecosystem, leveraging LinkedIn, and advanced job search techniques to land your dream AI PM role.
Meet Your Instructor
Ata Tahiroglu
AI/ML Group Product Manager @ Apple
Columbia University Lecturer
Ata is a seasoned AI/ML Group Product Manager at Apple with extensive experience building cutting-edge AI products at scale. He has lectured over 2k students over the last 4 years. Based in the San Francisco Bay Area, he brings real-world expertise from one of the world's leading technology companies to help you master the art and science of AI product management.
Next Cohort
Why This Masterclass?
Tools You Will Master
Real Case Studies
Dive deep into actual AI product successes and failures. Learn from real-world implementations across different industries.
What Our Students Are Saying
"This masterclass transformed my understanding of AI product development. The hands-on projects were invaluable, and the instructor's real-world insights were truly inspiring."
Jane Doe
Senior Product Manager, Tech Company
"I came in with a traditional PM background and left feeling confident about leading AI initiatives. The curriculum is incredibly practical and directly applicable to today's AI landscape."
John Smith
Product Lead, AI Startup
"The Capstone Project was a game-changer. Building a real AI product from scratch, with expert guidance, gave me the confidence and portfolio piece I needed to advance my career."
Emily White
Aspiring AI Product Manager
Ready to Master AI Product Management?
Join the next generation of product managers who can confidently build, ship, and scale AI products that matter.
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