- New to the project? → CLAUDE.md for setup and overview
- Looking for current work? → PROJECT_PLAN.md
- Looking for implementation details? → CLAUDE.md - Task Navigation
ROADMAP.md is your strategic compass:
- Where are we going? (vision and goals)
- Why does it matter? (business/technical rationale)
- What are we building? (quarterly initiatives)
This is NOT a detailed task list or sprint plan - see PROJECT_PLAN.md for tactical execution.
Review: Quarterly or when strategic priorities shift
[Your project name] aims to [high-level vision - what impact will this have?].
Success looks like:
- [Outcome 1 - e.g., "95%+ model accuracy on production data"]
- [Outcome 2 - e.g., "Automated retraining pipeline reducing manual effort by 80%"]
- [Outcome 3 - e.g., "Real-time predictions serving 10K+ requests/day"]
Strategic Goal: [What we're achieving this quarter - e.g., "Establish ML pipeline foundation"]
Key Initiatives:
-
[Initiative 1 - e.g., "Data preprocessing and feature engineering pipeline"]
- Why: [Business/technical reason - e.g., "Required to establish reproducible model training"]
- Impact: [Expected outcome - e.g., "Reduce data prep time from days to hours"]
- Dependencies: [Any blockers - e.g., "Production dataset access pending compliance approval"]
-
[Initiative 2 - e.g., "Baseline model development and evaluation"]
- Why: [Reason - e.g., "Establish performance benchmarks for future improvements"]
- Impact: [Outcome - e.g., "First production-ready model with documented performance metrics"]
- Dependencies: [Blockers - e.g., "Depends on completing Initiative 1"]
Success Metrics:
- [Metric 1 - e.g., "Data pipeline processes 100K records/hour"]
- [Metric 2 - e.g., "Baseline model achieves ≥85% accuracy"]
Themes: [High-level focus areas - no detailed breakdown yet]
Example themes:
- Model deployment and serving infrastructure
- A/B testing framework for model comparison
- Monitoring and observability for ML systems
Detailed initiatives will be defined as Q1 progresses.
Where we're heading: [1-2 paragraphs describing aspirational goals]
Example: "By end of 2025, we aim to have a fully automated ML pipeline capable of continuous training, evaluation, and deployment. This includes automated feature engineering, model selection, hyperparameter tuning, and production deployment with rollback capabilities. Success means data scientists spend less time on infrastructure and more time on model innovation."
Key milestones (subject to change):
- Automated retraining pipeline (H2 2025)
- Multi-model A/B testing framework (H2 2025)
- Real-time feature serving (2026)
Use this section to document major architectural or technical choices that shape the roadmap.
Decision: [What was decided - e.g., "Use scikit-learn/PyTorch with MLflow for flexibility, deploy on cloud infrastructure"]
Rationale:
- For: [Reasons - e.g., "Team expertise in Python, more control over model implementation, vendor independence"]
- Against: [Trade-offs - e.g., "More infrastructure work than fully managed solutions"]
Review when: [Trigger - e.g., "Team size doubles or cloud costs exceed $X/month"]
Decision: [What was decided - e.g., "Start with batch predictions, add real-time serving in Q3"]
Rationale:
- For: [Reasons - e.g., "Simpler to implement, meets current business needs"]
- Against: [Trade-offs - e.g., "May need architecture changes for real-time later"]
Review when: [Trigger - e.g., "Business requests <1 second prediction latency"]
Last Updated: [YYYY-MM-DD] Next Review: [Date - typically quarterly]
Related Documentation:
- PROJECT_PLAN.md - Current tactical execution
- CLAUDE.md - Implementation guidance
- CHANGELOG.md - Completed feature history