Why this matters
MLOps Engineers sit at the intersection of Data Science (DS) and Platform/Infra teams. Most delays, incidents, and rework happen not because of code quality, but because collaboration breaks: unclear handoffs, missing requirements, or mismatched expectations. Mastering collaboration accelerates shipping models safely and reduces production risk.
- Real task: turn a DS notebook into a reliable service with monitoring.
- Real task: coordinate feature store changes across teams without breaking jobs.
- Real task: run a safe rollout (shadow/canary/A-B) and communicate it clearly.
- Real task: respond to data or model drift with DS and SRE on the same timeline.
Concept explained simply
Collaboration in MLOps is agreeing on contracts at every seam: data, model, service, and operations. Each contract states who owns what, the interface, quality targets, and how to change it safely.
Mental model
Think of your ML system as a relay race:
- Data Platform passes clean, versioned data (data contract).
- DS passes a reproducible model (model contract).
- MLOps passes a scalable, observable service (service contract).
- Platform/SRE passes reliable infra and incident process (ops contract).
At each handoff, you agree on inputs, outputs, SLOs, change process, and rollback plan.
Who this is for and prerequisites
Who this is for
- Early-career MLOps Engineers who need to coordinate DS and platform work.
- DS/ML Engineers transitioning to productionization.
- Platform/SREs supporting ML systems.
Prerequisites
- Basic Git, CI/CD, containers.
- Familiarity with model training, metrics, and inference patterns (batch/online).
- Comfort with incident basics (alerts, severity, on-call rotation).
Learning path
- Map ownership: draft a simple RACI for data, model, service, and operations.
- Define contracts: start with a minimal model contract and data validation checks.
- Standardize handoffs: add a PR checklist, README, and release notes template.
- Run a safe rollout: practice shadow or canary with rollback steps.
- Practice incidents: run a 30-minute drift simulation with DS and platform roles.
Worked examples
1) Clean handoff from DS to MLOps using a model contract
Scenario: DS trained a churn model. You need a reliable deployment.
- Agree on the interface: input fields, types, null policy, versioning.
- Record training context: data snapshot, features, seed, training command, metrics.
- Operational expectations: latency p95, throughput, warmup, batch size.
- Risk/rollback: acceptable metric floor and rollback trigger.
Result: a one-page model contract attached to the PR, allowing MLOps to automate packaging, CI tests, and deploy gates.
2) Coordinating a drift incident
Scenario: Monitoring shows data drift on two key features and a 5% AUC drop.
- MLOps opens an incident thread with severity, impact, timeframe, and owners.
- DS investigates feature distribution shift; Platform checks data pipeline freshness and schema.
- Decision: switch to the previous model or enable feature fallback; schedule retraining if root cause is real-world shift.
Outcome: clear roles, a timestamped update, and a rollback executed within SLO.
3) Requesting a new feature in the feature store
Scenario: DS wants a new aggregation feature.
- Create an RFC: feature definition, windowing, expected cardinality, update cadence.
- Platform reviews storage cost, compute plan, and backfill.
- MLOps adds validation, lineage tags, and a deprecation plan for older features.
Outcome: predictable timelines and cost-aware choices.
4) Safe rollout plan for a major model update
Scenario: Big uplift expected, risk unknown.
- Plan: shadow traffic for 3 days, then canary to 10%, then 50%, then 100%.
- Gates: quality metrics within tolerance, error rate no higher than baseline.
- Rollback: single command to revert image tag; alert DS if gate fails.
Outcome: business moves forward without betting the farm on day one.
Collaboration templates you can copy
Minimal model contract checklist
- Model name and version
- Task and target definition
- Input schema (name, type, allowed nulls, ranges)
- Output schema (class labels or regression range)
- Training data snapshot/version and time window
- Training command and seed
- Key metrics (train/val/test) and minimum acceptable in prod
- Latency/throughput expectations
- Dependencies and artifact locations
- Monitoring plan (data quality, performance, drift)
- Rollback criteria and previous stable version
Handoff PR checklist
- README with how to train, package, and run locally
- Dockerfile or environment spec
- Unit tests for preprocessing and inference
- Contract file checked in (model_contract.yaml)
- Sample requests/responses and golden test data
- Release notes: what changed, why, risk, rollback
RFC outline for data/feature changes
- Context and motivation
- Precise definition and owners
- Impact analysis (jobs, storage, cost)
- Migration plan and deprecation timeline
- Validation strategy (backfill checks, schema tests)
- Success criteria and monitoring
Incident update template
- Summary: what is broken and who is impacted
- Timeline: when it started, detection time
- Current status: mitigations in place
- Hypotheses: likely causes
- Actions: who is doing what by when
- Next update time
Simple RACI (no table needed)
- Data contract: Responsible (Data Platform), Accountable (Data Lead), Consulted (MLOps), Informed (DS)
- Model contract: Responsible (DS), Accountable (DS Lead), Consulted (MLOps), Informed (Platform)
- Service deployment: Responsible (MLOps), Accountable (MLOps Lead), Consulted (Platform), Informed (DS)
- Monitoring and on-call: Responsible (MLOps/Platform), Accountable (SRE Lead), Consulted (DS), Informed (Product)
Exercises
Do these to build muscle memory. A quick test is available to everyone; sign in to save your progress automatically.
- Exercise 1: Write a minimal model contract for a churn model handoff to MLOps. Include interface, training context, ops expectations, and rollback.
- Exercise 2: Draft an incident update for a detected performance drop with suspected data drift.
Exercise 1 — guidance
- Keep it to ~12 bullet points.
- Make inputs explicit (types, nulls, ranges).
- Define a rollback trigger based on a metric.
Exercise 2 — guidance
- State impact plainly.
- Assign owners and deadlines.
- Promise the time of your next update.
Self-check checklist
- [ ] Does your model contract define input/output schemas and minimum metrics?
- [ ] Is the training data snapshot reproducible?
- [ ] Are latency and throughput targets clear?
- [ ] Is there a single-command rollback plan?
- [ ] Does your incident note include next update time and named owners?
Common mistakes and how to self-check
- Vague inputs: “user features” instead of listing names and types. Fix: write explicit schema.
- No rollback: assuming the new model will be fine. Fix: keep last-good tag and trigger.
- Skipping DS in incident response: fixing infra while the model is drifting. Fix: include DS owner and hypothesis step.
- Silent feature changes: breaking jobs with an unnoticed schema change. Fix: RFC + deprecation window and validation tests.
- Unrealistic SLOs: latency too tight for current infra. Fix: align with Platform on capacity and test under load.
Practical projects
- Handoff-in-a-box: Build a toy repo with model_contract.yaml, Dockerfile, unit tests, and a CLI to run shadow predictions.
- Release pipeline guardrails: Add CI checks that fail if the model contract is missing fields or golden tests regress.
- Drift game day: Simulate a feature shift in staging and practice incident roles, updates, and rollback.
Mini challenge
In your next model change, run this lightweight process:
- [ ] Open a short RFC describing the change and risks.
- [ ] Attach a minimal model contract to the PR.
- [ ] Do a 10% canary with a rollback command ready.
- [ ] Post a single incident-style update if anything deviates from plan.
Note: The quick test is available to everyone. Sign in if you want your progress saved automatically.
Next steps
- Adopt the model contract and handoff checklist in your team’s next PR.
- Schedule a 30-minute drift simulation with DS and Platform.
- Automate one guardrail (schema validation or golden test) in CI this week.