π§ Machine Learning Project Plan (End to End) - liniribeiro/machine_learning GitHub Wiki
π§ PHASE 1: Discovery & Problem Definition
π Understand the Problem Space
- What problem are we solving?
- Who are the stakeholders and end users?
- What is the impact of solving (or not solving) this problem?
- What are the business KPIs tied to this problem?
- Is this problem best suited for ML, or is there a simpler rule-based solution?
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Deliverables
- Clear problem statement
- Target outcome (qualitative & quantitative)
- Success criteria (e.g., increase CTR by 5%, reduce churn by 10%)
π PHASE 2: Data Strategy
π§ Understand the Data
- What data is available today?
- What is the source of truth?
- What features/labels do we have?
- Do we need to collect new data?
- Is the data labeled? If not, whatβs the labeling strategy?
- How will we ensure data quality and freshness?
π§° Steps
- Data audit and cataloging
- Exploratory Data Analysis (EDA)
- Identify gaps (coverage, bias, imbalance)
- Define feature engineering plan
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Deliverables
- Data schema and source map
- Feature list
- Labeling/ground-truth strategy
- Data contracts (if needed)
ποΈ PHASE 3: Solution Design
π§ Define ML Approach
- Is this supervised, unsupervised, or reinforcement learning?
- What models are suitable for the data/problem?
- Do we need real-time or batch inference?
- What are the baseline and upper bounds (e.g., human-level performance)?
- Will we build from scratch, use pre-trained models, or fine-tune?
𧩠Additional Considerations
- Privacy & compliance (e.g., GDPR)
- Fairness, bias, and explainability
- Model observability requirements
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Deliverables
- Chosen ML approach with justification
- Evaluation metrics
- Baseline definition
- Infrastructure and tooling requirements
π§ͺ PHASE 4: Prototyping & Experimentation
𧬠Build & Validate
- Develop MVP model(s)
- Define data splits (train/test/validation)
- Conduct offline experiments
- Validate data pipelines
π Key Questions
- How do we evaluate model performance?
- Is the model robust under data drift or edge cases?
- Is inference time acceptable for our use case?
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Deliverables
- MVP model artifacts
- Evaluation reports
- Decision to move forward or iterate
π PHASE 5: Integration & Deployment
π§ Engineering Integration
- Define how the model will be used in production (API, batch, SDK)
- Work with dev/backend/frontend/infra teams
- Create a rollback and versioning strategy
- Deploy to staging
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Deliverables
- Deployment plan
- CI/CD pipelines
- Scalable infrastructure setup
- Logging and monitoring hooks
π PHASE 6: Monitoring & Feedback Loop
π Observe & Iterate
- Are we tracking real-world model performance?
- Do we observe data or model drift?
- How is feedback collected for retraining?
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Metrics to Track
- Model accuracy/performance in production
- Latency and throughput
- Drift detection (input/output)
- End-user engagement or success KPIs
π Tools & Methods
- Model monitoring (e.g., Evidently, WhyLabs)
- Logs, dashboards, and alerts
- Shadow deployments for model comparison
π PHASE 7: Iteration, Maintenance & Scaling
- Define retraining cadence or triggers
- Experiment with new features or models
- Optimize or expand model coverage
- Scale to new markets, languages, use cases
- Ensure full documentation and handover plans
π§ Cross-Functional Considerations
π₯ Collaboration
- Stakeholder alignment and demos
- Risk and impact analysis
- Communication of uncertainty
- Documentation for product, engineering, and business
π« Go-To-Market / Rollout Strategy
- How will this feature be launched?
- Who needs training or onboarding?
- What feedback loop is in place post-launch?