Why this matters
Great prototypes die in notebooks if teams cannot run, monitor, and iterate on them in production. Clear handoffs reduce rework, prevent outages, and speed up customer impact.
- You define what "good" looks like via metrics, acceptance tests, and SLAs.
- You provide reproducible code, data lineage, and model artifacts so engineers can deploy safely.
- You align stakeholders on scope, risks, and rollback plans.
Who this is for
- Applied Scientists preparing models to be integrated by MLE/Platform teams.
- Data/ML Engineers receiving research prototypes.
- Product Managers and QA who need clear acceptance criteria.
Prerequisites
- Comfort with training/evaluating ML models and basic error analysis.
- Version control (e.g., Git), environment management, and unit testing.
- Understanding of data schemas, feature pipelines, and basic monitoring metrics.
Concept explained simply
A production handoff is a compact bundle of artifacts + contracts + agreements that makes your prototype deployable and supportable.
- Artifacts: model files, code, environment spec, datasets, and documentation.
- Contracts: API input/output schema, latency and throughput targets, ids and traceability.
- Agreements: acceptance criteria, monitoring plan, rollback plan, and owner contacts.
Mental model
Think of handoff as a flight pre-check:
- Pack: everything needed to fly (artifacts).
- Plan: route and weather (dependencies and risks).
- Check: instruments (tests and monitors).
- Brief: crew (owners, on-call, runbook).
Handoff essentials (what to include)
1) Model and code artifacts
- Model binary and version tag; training code that can reproduce the binary.
- Environment spec (e.g., conda/requirements.txt) with exact versions and random seeds.
- Small reproducible test dataset with expected outputs for smoke tests.
- Automated tests: unit tests for pre/post-processing, integration test for full inference path.
2) Data and feature contracts
- Explicit schema: types, ranges, allowed nulls, categorical domains, and timezone/locale notes.
- Feature provenance: where features come from, refresh cadence, and known leakage risks.
- Drift-sensitive fields flagged for monitoring.
3) API contract (if serving online)
- Request/response examples with schema and field semantics.
- Latency budget, throughput targets, and idempotency requirements.
- Error codes, rate limits, and tracing/ids for observability.
4) Evaluation and acceptance criteria
- Primary/secondary metrics, test sets, and baselines.
- Acceptance thresholds (e.g., AUC, NDCG, recall@k) and guardrails (e.g., fairness bounds).
- Rollout plan: canary/A-B, sample sizes, and decision rules to graduate.
5) Monitoring and runbook
- SLIs/SLOs: availability, latency, error rate, drift indicators, output distribution, business KPI.
- Alert thresholds and dashboards to be created.
- Runbook: common issues, diagnostics, rollback procedure, owner rotation, escalation contacts.
6) Responsible AI and compliance
- Model card: intended use, limitations, known biases, safety mitigations.
- Data privacy: PII handling, retention policy, and access controls.
- Security notes: dependency risks, signing/verification of artifacts.
Worked examples
Example 1: Ranking model handoff to Search team
- Artifacts: LightGBM model v1.3.2, feature mapping, and training script with seed 42.
- Contract: Input requires query_id, doc_id, and 18 numeric features; output scores in [0,1].
- Acceptance: Must improve NDCG@10 by ≥2% vs baseline on last 30 days; latency p95 ≤ 30 ms.
- Monitoring: Feature drift on CTR_7d; alert if KL divergence > 0.2 for 24h.
- Rollout: 10% canary for 3 days; graduate if lift persists, no latency regressions, and no guardrail breach.
Example 2: Fraud classifier from batch to streaming
- Change: Features previously computed daily; now require 5-min aggregates.
- Plan: Replace 2 leakage-prone features, add real-time proxies, document feature drift risk.
- Acceptance: Recall@precision0.9 ≥ baseline; false-positive cost within budget threshold.
- Runbook: If Kafka lag > threshold, auto-fallback to baseline model; page on-call if lag persists > 15 min.
Example 3: Vision model notebook to microservice
- Artifacts: ONNX model, preprocessing library pinned, image normalization spec, and GPU requirement.
- API: POST /infer with base64 image; respond with top-3 classes and confidences.
- Tests: Golden images with expected class ids; p95 latency target 80 ms on T4 GPU.
- Responsible AI: Document failure modes on low-light images; add confidence threshold and abstain option.
Step-by-step handoff playbook
- Freeze the candidate model: tag code, data snapshot, and random seed.
- Create the handoff README: purpose, diagrams, and artifact index.
- Define contracts: data schema and API I/O with examples.
- Add tests: unit tests for transforms; integration test covering end-to-end inference.
- Specify acceptance criteria and rollout decision rules.
- Draft monitoring plan, dashboards to build, and alert thresholds.
- Complete model card and privacy notes.
- Run a dry run: engineer follows README to reproduce metrics on a clean machine.
- Hold a handoff review: walk through risks, finalize owners, and agree timeline.
Handoff checklist (copy/paste)
- Artifacts packaged and versioned (code, model, data sample, env spec).
- Repro steps validated by a teammate from scratch.
- Data/API contracts documented with examples and edge cases.
- Acceptance metrics, thresholds, and guardrails defined.
- Monitoring, alerts, and dashboards specified.
- Rollback plan and on-call ownership documented.
- Model card and privacy/security considerations completed.
- Sign-off from PM, DS, and Eng leads.
Exercises
Exercise 1: Write a minimal handoff README
Mirror of Exercise ex1 below. Draft a README for a binary classifier that flags risky transactions. Include purpose, artifact list, environment, data schema, evaluation metrics, and acceptance criteria.
Exercise 2: Define an API contract and acceptance tests
Mirror of Exercise ex2 below. Specify request/response schemas, latency targets, and two executable acceptance tests for a real-time inference endpoint.
Common mistakes and self-check
- Missing reproducibility: If a teammate cannot reproduce metrics within 1 hour, your README is incomplete.
- Ambiguous data schema: If fields lack units, types, or ranges, expect production bugs.
- Undefined rollback: If criteria to roll back are unclear, incidents last longer than needed.
- No guardrails: If fairness or safety bounds are absent, risk shipping harmful behavior.
- Monitoring gaps: If you cannot detect drift or outages within minutes, your plan is weak.
Self-check: Ask an engineer to follow your README on a clean environment. Time how long it takes, list friction points, and patch the docs.
Practical projects
- Package a small sklearn model with an API contract and smoke tests; have a peer deploy it locally.
- Convert a notebook image classifier to a containerized service with golden test images and a runbook.
- Create monitoring specs for data drift and latency for any existing model you own; simulate alerts.
Learning path
- Start: Create a lightweight handoff README for an existing prototype.
- Next: Add formal contracts (data, API), tests, and acceptance criteria.
- Then: Design monitoring and a rollback plan; conduct a dry run.
- Finally: Run a handoff review and iterate based on feedback.
Next steps
- Complete the exercises below and compare with the example solutions.
- Take the quick test to validate your understanding. The test is available to everyone; only logged-in users will have progress saved.
- Apply the checklist on your next project before the handoff meeting.
Mini challenge
In 10 bullet points or fewer, write a handoff plan for upgrading an existing production model with a new version that has better accuracy but 2x latency. Include how you will mitigate latency and define a go/no-go rule.