🧭 Roadmap to Become a PM for ML Features - liniribeiro/machine_learning GitHub Wiki

🧩 Phase 1: Strengthen Foundations (0–3 months)

📚 Learn Core ML Concepts

• Complete Andrew Ng's ML Course (Coursera)
• Take a Data Science for PMs course (Udemy / PMLeague)
• Learn about:
  • Supervised and unsupervised learning
  • Model evaluation (accuracy, precision, recall, F1)
  • Data labeling and feature engineering

🧰 Learn Tools (Not for coding mastery, just fluency)

• Google Colab or Jupyter Notebooks
• SQL (SELECT, JOIN, GROUP BY, WINDOW functions)
• Pandas & Scikit-learn (basic understanding)
• MLFlow or Weights & Biases (intro to tracking experiments)

🛠️ Apply Knowledge

• Run a simple ML experiment (e.g., churn prediction)
• Write a one-pager product brief on how to ship it
• Explain trade-offs: ML vs. heuristics

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🔍 Phase 2: Product Thinking for ML (3–6 months)

📖 Understand ML in Real Products

• Study examples of ML-powered features (e.g., recommendation, search, ranking)
• Learn the difference between rules-based and ML systems
• Read: Designing Machine Learning Systems by Chip Huyen (selected chapters)

🎯 Build Product Skills

• Define success metrics for ML features (e.g., CTR, precision, revenue impact)
• Write detailed user stories with edge cases for ML behavior
• Outline a model lifecycle: data → train → evaluate → serve → monitor

🤝 Cross-Functional Collaboration

• Set recurring 1:1s with a DS or ML engineer
• Ask how they assess data quality, retraining frequency, model versioning

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🧪 Phase 3: Experimentation & MLOps (6–12 months)

⚙️ Deepen Technical Awareness

• Learn A/B testing for ML (bucket testing, shadow testing)
• Understand:
  • Model monitoring (latency, accuracy, drift)
  • Bias and fairness detection
  • Model degradation

🧱 Apply via Projects

• Collaborate on a real A/B test for an ML model
• Define KPIs and success criteria with DS team
• Create a dashboard for model health metrics

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🚀 Phase 4: Strategic Vision & Leadership (12+ months)

📊 Lead Product Strategy

• Own ML product roadmap (e.g., ranking, personalization)
• Create decision frameworks for:
  • ML readiness
  • Trade-offs between complexity vs. value
  • Team capacity vs. impact

🌍 Organizational Skills

• Lead discussions on responsible AI (privacy, fairness)
• Review contracts and legal terms involving ML usage
• Mentor junior PMs or data team members

🧭 Thought Leadership

• Present ML product learnings at company-wide forums
• Write internal docs on model/product trade-offs and lessons learned

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📚 Bonus: Learning Resources

📘 Books

• Building Machine Learning Powered Applications – Emmanuel Ameisen
• Designing Machine Learning Systems – Chip Huyen
• Interpretable Machine Learning – Christoph Molnar

📝 Blogs & Podcasts

• Google AI Blog
• Netflix Tech Blog
• Stitch Fix Algorithms Blog
• [ ] Podcast: Data Skeptic
• [ ] Podcast: TWIML AI
• [ ] Podcast: Practical AI

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