Establish the foundational product management discipline that applies to all products before specialising in AI-specific challenges.
Curriculum
1User research: discovery interviews, contextual inquiry, surveys, usability testing, and persona development
2Problem discovery: Jobs-to-be-Done framework, opportunity assessment, and problem-solution fit
3Hypothesis-driven development: assumption mapping, experiment design, and validated learning cycles
4Roadmap planning: outcome-based roadmaps, now/next/later framework, and stakeholder alignment
Step 2beginner4-6 weeks
AI/ML Literacy for PMs
Build enough technical fluency to have productive conversations with ML engineers, ask the right questions, and set realistic expectations for AI capabilities.
Curriculum
1Supervised learning: classification, regression, training/validation/test splits, and label quality
2Unsupervised learning: clustering, dimensionality reduction, and anomaly detection use cases
3Model evaluation: accuracy, precision, recall, F1 score, AUC-ROC, and confusion matrices
4
Step 3intermediate4-6 weeks
Data Strategy
Understand that data is the fuel of AI products and learn to build sustainable data strategies that feed model development and improvement.
Curriculum
1Data collection: first-party vs third-party data, user-generated data, and data partnership models
2Data labeling: annotation workflows, inter-annotator agreement, active learning, and quality assurance
3Data quality assessment: completeness, consistency, accuracy audits, and data profiling
4Privacy regulations: GDPR, CCPA, data processing agreements, and privacy-preserving ML techniques
Step 4intermediate4-6 weeks
AI Product Design
Create AI-powered experiences that users understand, trust, and find genuinely useful, even when the AI makes mistakes.
Curriculum
1Human-AI interaction: augmentation vs automation, appropriate automation levels, and user control
2Explainability: feature importance, model explanations for users, and transparency in AI decisions
3Graceful degradation: fallback experiences when AI fails, confidence thresholds, and manual overrides
4Feedback loops: implicit vs explicit feedback, correction mechanisms, and continuous improvement
Step 5intermediate4-6 weeks
Metrics & Experimentation
Master the unique experimentation challenges of AI products where model performance metrics and user experience metrics must be aligned.
Curriculum
1A/B testing for ML: model comparison, interleaving experiments, and multi-armed bandit approaches
2Offline vs online metrics: correlation analysis, offline evaluation pitfalls, and online metric design
3Guardrail metrics: safety metrics, regression detection, and automated experiment safeguards
Build the ethical framework and practical toolkit to ensure your AI products are fair, transparent, and compliant with emerging regulations.
Curriculum
1Fairness: demographic parity, equalised odds, individual fairness, and fairness-accuracy trade-offs
2Bias detection: dataset bias audits, model bias testing, intersectional analysis, and debiasing techniques
3Transparency: model cards, data sheets, algorithmic impact assessments, and user-facing explanations
4
Step 7advanced6-8 weeks
LLM Product Development
Navigate the fast-moving landscape of LLM-powered products with practical strategies for building reliable, cost-effective, and trustworthy AI features.
Curriculum
1Prompt design: system prompts, few-shot examples, chain-of-thought, and prompt versioning
Training data: data collection strategies, labeling quality, class imbalance, and data augmentation
5Overfitting and underfitting: bias-variance trade-off, regularisation, and cross-validation
6AI limitations: correlation vs causation, distribution shift, adversarial examples, and failure modes
Tools & Platforms
Google AI/ML Crash CourseKaggle LearnTensorFlow PlaygroundScikit-learn documentation
🧠
Step 2beginner4-6 weeks
AI/ML Literacy for PMs
Build enough technical fluency to have productive conversations with ML engineers, ask the right questions, and set realistic expectations for AI capabilities.
Curriculum
1Supervised learning: classification, regression, training/validation/test splits, and label quality
2Unsupervised learning: clustering, dimensionality reduction, and anomaly detection use cases
3Model evaluation: accuracy, precision, recall, F1 score, AUC-ROC, and confusion matrices
4Training data: data collection strategies, labeling quality, class imbalance, and data augmentation
5Overfitting and underfitting: bias-variance trade-off, regularisation, and cross-validation
6AI limitations: correlation vs causation, distribution shift, adversarial examples, and failure modes
Tools & Platforms
Google AI/ML Crash CourseKaggle LearnTensorFlow PlaygroundScikit-learn documentation
5Synthetic data: generation techniques, domain randomisation, and when synthetic supplements real data
6Annotation tools: Label Studio, Scale AI, Amazon SageMaker Ground Truth, and build vs buy decisions
Tools & Platforms
Scale AI / LabelboxLabel StudioGreat Expectations (data quality)OneTrust (privacy)
💾
Step 3intermediate4-6 weeks
Data Strategy
Understand that data is the fuel of AI products and learn to build sustainable data strategies that feed model development and improvement.
Curriculum
1Data collection: first-party vs third-party data, user-generated data, and data partnership models
2Data labeling: annotation workflows, inter-annotator agreement, active learning, and quality assurance
3Data quality assessment: completeness, consistency, accuracy audits, and data profiling
4Privacy regulations: GDPR, CCPA, data processing agreements, and privacy-preserving ML techniques
5Synthetic data: generation techniques, domain randomisation, and when synthetic supplements real data
6Annotation tools: Label Studio, Scale AI, Amazon SageMaker Ground Truth, and build vs buy decisions
Tools & Platforms
Scale AI / LabelboxLabel StudioGreat Expectations (data quality)OneTrust (privacy)
5Confidence scores: communicating uncertainty, threshold calibration, and user-facing confidence UI
6Trust building: progressive disclosure of AI capabilities, accuracy communication, and error recovery
Tools & Platforms
Figma (AI UX prototyping)Google PAIR GuidelinesMicrosoft HAX ToolkitApple HI Guidelines for ML
🎨
Step 4intermediate4-6 weeks
AI Product Design
Create AI-powered experiences that users understand, trust, and find genuinely useful, even when the AI makes mistakes.
Curriculum
1Human-AI interaction: augmentation vs automation, appropriate automation levels, and user control
2Explainability: feature importance, model explanations for users, and transparency in AI decisions
3Graceful degradation: fallback experiences when AI fails, confidence thresholds, and manual overrides
4Feedback loops: implicit vs explicit feedback, correction mechanisms, and continuous improvement
5Confidence scores: communicating uncertainty, threshold calibration, and user-facing confidence UI
6Trust building: progressive disclosure of AI capabilities, accuracy communication, and error recovery
Tools & Platforms
Figma (AI UX prototyping)Google PAIR GuidelinesMicrosoft HAX ToolkitApple HI Guidelines for ML
5Experimentation platforms: feature flags, traffic allocation, and experiment analysis automation
6ML-specific metrics: model freshness, prediction latency, coverage, and calibration monitoring
Navigate the fast-moving landscape of LLM-powered products with practical strategies for building reliable, cost-effective, and trustworthy AI features.
Curriculum
1Prompt design: system prompts, few-shot examples, chain-of-thought, and prompt versioning