AI Product Management: From Strategy to Production

Scope deterministic vs. probabilistic systems across the ML lifecycle, from data to monitoring

Choose supervised, unsupervised, recommender, and forecasting approaches for real product needs

Set precision vs. recall trade-offs and fallback UI, then write your first portfolio piece: an AI feature PRD

Design proprietary data flywheels, closed feedback loops, and compliant synthetic data plans

Run labeling taxonomies, human-in-the-loop review, active learning, and vendor SLAs within a cost budget

Lead vision, OCR, and audio products, then build a multimodal data strategy and annotation blueprint

Choose between prompt engineering, RAG, and fine-tuning, and explain the trade-offs to your team

Design agent workflows with tool use, citations, undo buffers, and streaming UI that build user trust

Calculate cost per user interaction, set up LLM-as-a-judge evals, and write a GenAI architecture spec

Score AI bets with an AI-adapted RICE framework that weighs data readiness and regulatory risk

Audit dataset bias, map EU AI Act risk tiers, and monitor data and concept drift after launch

Turn model metrics into margin and ROI, then pitch a board-level AI roadmap with 3-year economics