| name | ml-workflow |
| description | Use when designing end-to-end ML workflows. Covers experiment tracking, feature engineering and storage, model training pipelines, serving and deployment, A/B testing, and drift monitoring. Do not use for data warehouse schema design (use schema-evaluation) or ETL pipeline architecture (use pipeline-design). |
| metadata | {"author":"dtsong"} |
ML Workflow
Purpose
Design end-to-end ML workflows covering experiment tracking, feature engineering and storage, model training pipelines, model serving and deployment, A/B testing for models, and monitoring for data and model drift. Produces a workflow architecture, tool selection rationale, and operational runbook.
Scope Constraints
Reads ML code, configuration files, experiment logs, and infrastructure specs for analysis. Does not train models, execute experiments, or deploy to production.
Inputs
- ML problem type (classification, regression, ranking, recommendation, NLP, CV)
- Data sources and feature candidates
- Model complexity range (linear/tree-based vs deep learning)
- Serving requirements (batch predictions, real-time inference, edge deployment)
- Team size and ML maturity (first model vs established ML platform)
- Infrastructure constraints (cloud provider, GPU availability, budget)
Input Sanitization
No user-provided values are used in commands or file paths. All inputs are treated as read-only analysis targets.
Procedure
Progress Checklist
Step 1: Define the ML Problem Clearly
Before any tooling decisions, formalize:
- What is the prediction target? What does "correct" look like?
- What is the business metric this model optimizes? (Not just accuracy — revenue, conversion, engagement)
- What is the baseline? (Rule-based heuristic, current model, random chance)
- What is the minimum viable performance to ship?
Document the problem statement, target variable, evaluation metric, and success threshold.
Step 2: Design the Feature Engineering Pipeline
Map raw data to model-ready features:
- Feature identification: Which raw fields become features? What transformations are needed (encoding, scaling, windowing, embedding)?
- Temporal features: Aggregations over time windows (last 7 days, last 30 days). Guard against leakage — never use future data to predict the past.
- Feature store evaluation: Does this project warrant a feature store (Feast, Tecton, Hopsworks)? Feature stores add value when: features are shared across models, real-time features are needed, or training-serving skew is a risk.
- Feature documentation: Each feature should have: name, description, data type, source, transformation logic, and expected distribution.
Step 3: Design Experiment Tracking
Set up reproducible experiment management:
- Tool selection: MLflow (open-source, self-hosted), Weights & Biases (managed, rich visualization), Neptune, or ClearML.
- What to track: Hyperparameters, metrics (train/val/test), dataset version, code version (git SHA), environment (dependencies), artifacts (model files, plots).
- Experiment organization: Project → Experiment group → Individual runs. Name runs meaningfully (not "run_42").
- Comparison workflow: How does the team compare runs? Dashboard? Automated reports?
Step 4: Design the Training Pipeline
Build a reproducible, automated training workflow:
- Data split strategy: Time-based splits for temporal data, stratified splits for imbalanced classes. Never random-split time-series data.
- Training orchestration: Single script, or DAG-based (Airflow, Kubeflow Pipelines, SageMaker Pipelines)?
- Hyperparameter tuning: Grid search, random search, Bayesian optimization (Optuna, Ray Tune)?
- Validation strategy: Cross-validation, holdout, or time-series walk-forward?
- Model registry: Where are trained models stored? How are they versioned? Who approves promotion to production?
Step 5: Design Model Serving
Plan how predictions reach users:
- Batch serving: Run predictions on a schedule, store results in a table. Best for recommendations, risk scores, daily reports.
- Real-time serving: Model behind an API endpoint. Best for search ranking, fraud detection, dynamic pricing.
- Streaming serving: Model embedded in a stream processor. Best for event-driven predictions on Kafka/Kinesis streams.
- Edge serving: Model deployed to device/browser. Best for latency-critical or offline-capable applications.
For real-time serving, specify: latency SLA (p50/p99), throughput (requests/second), scaling strategy (auto-scale triggers), and fallback behavior (what happens if the model is unavailable?).
Step 6: Design A/B Testing for Models
Plan controlled rollout of model changes:
- Traffic splitting: How is traffic divided between control (current model) and treatment (new model)?
- Metric selection: Primary metric (business KPI), guardrail metrics (latency, error rate), and minimum detectable effect.
- Duration calculation: How long must the test run to reach statistical significance?
- Rollback criteria: What triggers an automatic rollback?
Step 7: Design Monitoring and Drift Detection
Plan ongoing model health monitoring:
- Data drift: Monitor input feature distributions for shifts. Tool options: Evidently, WhyLabs, Great Expectations.
- Model drift: Monitor prediction distribution and performance metrics over time. Alert when performance degrades below threshold.
- Concept drift: Monitor the relationship between features and target. Retrain triggers when the world changes (seasonality, market shifts).
- Operational monitoring: Latency, error rates, throughput, GPU utilization for serving infrastructure.
Define retraining policy: scheduled (weekly/monthly), triggered (drift detected), or continuous (online learning).
Compaction resilience: If context was lost during a long session, re-read the Inputs section to reconstruct what system is being analyzed, check the Progress Checklist for completed steps, then resume from the earliest incomplete step.
Handoff
- Hand off to pipeline-design if the workflow reveals data ingestion or ETL orchestration needs.
- Hand off to operator/deployment-plan if model serving surfaces deployment or infrastructure architecture concerns.
Output Format
# ML Workflow: [Project/Model Name]
## Problem Definition
| Aspect | Detail |
|--------|--------|
| Problem type | ... |
| Target variable | ... |
| Business metric | ... |
| Evaluation metric | ... |
| Baseline performance | ... |
| Success threshold | ... |
## Feature Engineering
| Feature | Source | Transformation | Type | Leakage Risk |
|---------|--------|---------------|------|-------------|
| ... | ... | ... | ... | Low/Med/High |
**Feature store:** [Yes/No — tool choice and rationale]
## Experiment Tracking
| Aspect | Choice | Rationale |
|--------|--------|-----------|
| Tool | ... | ... |
| What's tracked | ... | ... |
| Organization | ... | ... |
## Training Pipeline
[ASCII diagram showing data → features → train → evaluate → register]
| Stage | Tool/Method | Notes |
|-------|------------|-------|
| Data split | ... | ... |
| Training | ... | ... |
| Tuning | ... | ... |
| Validation | ... | ... |
| Registry | ... | ... |
## Model Serving
| Aspect | Detail |
|--------|--------|
| Serving mode | Batch / Real-time / Streaming / Edge |
| Latency SLA | ... |
| Throughput | ... |
| Scaling | ... |
| Fallback | ... |
## A/B Testing
| Aspect | Detail |
|--------|--------|
| Traffic split | ... |
| Primary metric | ... |
| Guardrail metrics | ... |
| Min duration | ... |
| Rollback criteria | ... |
## Monitoring and Drift
| Monitor | Tool | Threshold | Action |
|---------|------|-----------|--------|
| Data drift | ... | ... | ... |
| Model drift | ... | ... | ... |
| Concept drift | ... | ... | ... |
| Operational | ... | ... | ... |
**Retraining policy:** [Scheduled / Triggered / Continuous — details]
Quality Checks
Evolution Notes
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