Artificial Intelligence & Data Science · 1.11

AI Product Manager

Bridge the gap between technical AI teams and business outcomes — scoping AI products, managing model lifecycles, and driving adoption.

4–5 months5 phases24 skills

Phase 01 · 5h

Product Management Foundations

Product thinking

Product thinkinguser problems vs solutions, Jobs-to-be-Done (JTBD) framework, outcome-driven innovation, product-market fit indicators

Discovery

Discoveryuser interviews (5-question framework: problem, current solution, frequency, willingness to pay, ideal outcome), surveys with Typeform, competitive analysis, market sizing (TAM/SAM/SOM using top-down and bottom-up methods)

Prioritization

PrioritizationRICE scoring (Reach × Impact × Confidence / Effort), MoSCoW (Must/Should/Could/Won't), Kano model (basic vs performance vs delight features), opportunity scoring matrix

Roadmapping

Roadmappingnow/next/later format (avoids false precision of timeline-based roadmaps), outcome-based roadmaps tied to OKRs, communicating roadmaps to executives vs engineers vs customers

Agile product development

Agile product developmentsprint planning, backlog grooming, writing user stories with acceptance criteria (Given/When/Then), defining Definition of Done

Phase 02 · 7h

AI/ML Literacy

ML types and when to use each

ML types and when to use eachsupervised (labeled data → predictions), unsupervised (finding patterns without labels), reinforcement learning (learning from rewards — robotics, game AI); classification vs regression; common algorithms (decision trees, random forests, gradient boosting, neural networks) — understand at product decision level, not implementation level

Data pipelines

Data pipelineshow data flows from collection → labeling → cleaning → feature engineering → training → evaluation, data quality issues (missing values, label noise, class imbalance), data flywheel effect (more users → more data → better models → more users)

Model evaluation for PMs

Model evaluation for PMsaccuracy vs precision vs recall (trade-offs — spam filter needs high precision to avoid blocking legitimate email; disease screening needs high recall to catch all cases), AUC-ROC curves, confusion matrix interpretation, A/B testing ML features

LLM fundamentals

LLM fundamentalstransformer architecture (conceptual), prompt engineering basics, fine-tuning vs RAG trade-offs (fine-tuning for consistent behavior, RAG for dynamic knowledge), context windows, token costs, hallucination risks

AI infrastructure awareness

AI infrastructure awarenessGPUs, cloud ML services (SageMaker, Vertex AI), model serving, cost structure of inference

Phase 03 · 8h

AI Product Strategy

AI-native vs AI-enhanced products

AI-native vs AI-enhanced productsidentifying where ML adds real value vs where heuristics suffice, avoiding "AI for AI's sake"

Data strategy

Data strategycold start problem (how to get initial training data before you have users), data moats (proprietary data as competitive advantage), privacy constraints (what data can you use, how must you handle it), data labeling cost estimation

Human-in-the-loop design

Human-in-the-loop designsetting confidence thresholds (high confidence → auto-process, low confidence → human review), designing fallback paths when the model fails, escalation workflows

Responsible AI

Responsible AIbias auditing with Fairlearn (demographic parity, equalized odds), explainability (SHAP values show feature contributions to each prediction, LIME for local explanations), model cards (documenting intended use, training data, performance metrics, known limitations)

AI product metrics

AI product metricsmodel performance metrics (accuracy, latency) vs product performance metrics (user satisfaction, task completion rate, revenue impact), leading vs lagging indicators

Build vs buy vs fine-tune

Build vs buy vs fine-tunecost modeling (API costs at volume vs self-hosted infrastructure), vendor evaluation (OpenAI, Anthropic, Cohere, open-source), time-to-market trade-offs

Regulatory landscape

Regulatory landscapeEU AI Act (risk-based classification — unacceptable, high, limited, minimal), NIST AI Risk Management Framework, industry-specific rules (HIPAA for health, Fair Lending for finance)

Phase 04 · 5h

Execution & Cross-Functional Leadership

Working with ML engineers

Working with ML engineerswriting ML-aware PRDs (define problem, success metrics, data requirements, model constraints, failure modes, evaluation plan), reviewing model performance reports, making go/no-go decisions based on metrics

Experiment design

Experiment designA/B testing ML features (define primary metric, guardrail metrics, sample size, duration), interpreting results with statistical significance, progressive rollout with feature flags

MLOps awareness

MLOps awarenessCI/CD for models (training → evaluation → deployment pipeline), monitoring for drift (data drift, model performance degradation), retraining triggers, rollback strategies

Stakeholder communication

Stakeholder communicationtranslating "model accuracy improved from 0.82 to 0.89" into business impact ("7% fewer support tickets escalated to humans, saving $50K/month"), executive updates, board-level AI strategy presentations

Phase 05 · 10h

Capstone

Capstone Project 1

Capstone Project 1End-to-end AI product strategy — problem identification, data audit, model approach selection, success metrics, MVP scope, phased roadmap with OKRs, risk mitigation plan

Capstone Project 2

Capstone Project 2Build vs buy decision with cost projections — compare fine-tuning an open-source LLM vs using Claude API vs building a traditional ML model, including 3-year cost model, risk analysis, and recommendation

Capstone Project 3

Capstone Project 3Responsible AI audit of an existing AI product — identify bias risks, test with Fairlearn, write a model card, propose mitigation strategies

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