Key Takeaway
ML pipeline design docs should explicitly document data dependencies and retraining triggers, because these are the most common sources of production pipeline failures.
When to Use This Template
Use this design doc when proposing a new ML training and serving pipeline, planning a major refactoring of an existing pipeline, or integrating a new model into an existing ML platform. This template covers the full lifecycle from data ingestion through model serving, with particular emphasis on reproducibility, experiment tracking, and operational readiness.
Template Sections
Define the business context, the ML task, success metrics, and explicit scope boundaries. Include: Business problem (what business outcome this pipeline supports), ML task definition (classification, regression, generation, ranking, etc.), Success metrics (model performance thresholds, latency, throughput), Scope boundaries (what this pipeline does and does not include), and Dependencies (upstream data sources, downstream consumers).
Document the complete data flow: Sources (data catalog entries, access patterns, freshness), Preprocessing (cleaning, normalization, deduplication steps), Feature engineering (feature definitions, computation logic, feature store integration), Quality gates (validation rules, failure handling, data drift detection), and Versioning (how training datasets are versioned and how to reproduce a specific training run).
Document the training infrastructure and workflow: Orchestration (pipeline tool, DAG structure, scheduling), Training infrastructure (compute type, instance count, distributed training strategy), Hyperparameter strategy (search method, parameter space, early stopping criteria), Experiment tracking (tool, metric logging, artifact storage), and Reproducibility (random seeds, environment pinning, dataset versioning).
Document how models are evaluated and promoted to production: Benchmark datasets (composition, refresh schedule, versioning), Metric definitions (primary and secondary metrics, thresholds for promotion), Baseline comparisons (current production model, rules-based baseline, human performance), Model registry workflow (registration, approval, staging), Deployment strategy (canary, blue-green, shadow mode), and Rollback procedure (automatic rollback triggers, manual rollback process).
Document the ongoing operational requirements: Monitoring (model quality metrics, data quality metrics, infrastructure metrics), Retraining schedule (trigger conditions, frequency, approval workflow), On-call (ownership, runbooks, escalation), Compute budget (training costs, serving costs, storage costs by month), Timeline (milestones from design to production), and Team allocation (roles needed, time commitment, skill gaps).
ml-pipeline-design-doc.md
MD · 10 KB
Complete ML pipeline design document template in Markdown format
Include a data lineage diagram showing every data transformation from source to model input. This single artifact prevents most data-related pipeline debugging because it makes dependencies visible and traceable.
Version History
1.0.0 · 2026-03-01
- • Initial ML pipeline design document template