How to Build an MLOps checklist (2026)

1) What is a MLOps checklist in 2026?

An MLOps checklist is a set of repeatable gates you use before a model ships, while it runs, and when it changes.
It blends DevOps controls with ML-specific needs like data validation, model evaluation, drift detection, and retraining triggers.
A good checklist is not a document you ignore.
It is a pipeline you can run on every change.
Google’s MLOps guidance frames MLOps as applying DevOps principles to ML, with CI, CD, and continuous training.
That framing is still the simplest mental model for 2025 teams. [Source](https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning)

Core outcomes your checklist should guarantee

• Reproducibility: same inputs produce the same model artifact.
• Reliability: predictable latency, stable APIs, controlled rollouts.
• Risk control: privacy, compliance, and “known failure modes” are handled.
• Observability: you detect degradation before users do.

2) Why does an MLOps checklist matter now?

The simplest reason is scale.
McKinsey reports 72% AI adoption across organizations in early 2024, after years near 50%.
The same survey reports 65% of respondents say their organizations regularly use generative AI.
More adoption means more models in production and more incidents waiting to happen without automation.
McKinsey also reports 44% of respondents say their organizations experienced at least one negative consequence from gen AI use, and inaccuracy is commonly cited.
That is a direct argument for stronger evaluation and monitoring gates. [Source](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-2024)

There is also engineering friction.
Gartner reports 77% of engineering leaders identify building AI capabilities into applications as a significant or moderate pain point.
This pushes teams toward platforms and standardized pipelines instead of one-off notebooks and manual deploys. [Source](https://www.gartner.com/en/newsroom/press-releases/2025-05-22-gartner-survey-finds-77-percent-of-engineering-leaders-identify-ai-integration-in-apps-as-a-major-challenge)

3) How do you validate data before training?

Treat data like production code.
Your checklist should verify schema, missingness, distribution shifts, leakage risks, and labeling quality.
In practice, this is a “stop-the-line” gate because bad data creates misleading models fast.
Google’s MLOps guidance calls out data schema skews and data value skews as triggers to stop or retrain.
That gives you a clean, testable policy. [Source](https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning)

4) How do you make training reproducible?

Reproducibility is your insurance policy.
You want to answer, “What exactly produced this model?” in minutes.
The MLOps Principles page emphasizes versioning across code, data, and models as first-class citizens.
It also lists compliance and audit as reasons you must access older versions fast.
That is not theory.
It is how teams survive incidents and regulatory reviews.

1. Pin dependencies and record the full environment (lockfiles + container digest).
2. Version datasets and features, not just code.
3. Track experiment parameters, metrics, and artifacts.
4. Write every model artifact to a registry with immutable IDs.
5. Record lineage: data version → training code commit → model version.

5) What should you test in ML systems?

Test coverage in ML is broader than unit tests.
The ML-Ops.org guide separates testing for data/features, model development, and ML infrastructure.
It also includes monitoring checks as part of production readiness.
This is useful because it tells you what to automate first. [Source](https://ml-ops.org/content/mlops-principles)

6) How do you set up CI/CD for ML pipelines?

CI/CD for ML is not only “build and deploy.”
Google’s MLOps reference explains that CI expands to testing and validating data and models.
CD becomes delivery of the whole training pipeline, which then deploys the prediction service.
It also defines maturity levels from manual workflows to full CI/CD automation. [Source](https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning)

7) How do you deploy models safely?

Safe deployment is about controlled exposure.
You want to support canaries, A/B testing, rollbacks, and API compatibility checks.
Google’s guidance explicitly calls out validating compatibility with target infrastructure, testing prediction service APIs, and load testing for QPS and latency.
Use that as your default deployment gate. [Source](https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning)

Deployment checklist (minimal, but strong)

• Model artifact is immutable and stored in a registry.
• Serving contract tests pass (inputs/outputs + schema).
• Canary deploy to 1–5% traffic for at least 24 hours.
• Rollback button is tested monthly.
• Latency budgets are enforced (P95 + P99).

8) What should you monitor in production?

Monitoring is the part teams skip, then regret.
McKinsey reports negative consequences from gen AI use are already common, with inaccuracy showing up most often.
If you do not monitor, you do not learn until customers complain. [Source](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-2024)

9) How do you handle governance and responsible AI?

Governance is not a separate project.
It is another set of gates.
Harvard Business Review highlights a practical approach: bake responsible AI into development cycles early, so teams avoid disruptive adjustments later.
In the HBR example, Deutsche Telekom integrated responsible AI principles into operations and anticipated regulation changes. [Source](https://hbr.org/2024/05/how-to-implement-ai-responsibly)

Governance checklist (minimum viable)

• Document purpose, intended users, and out-of-scope uses.
• Track training data provenance and licensing constraints.
• Run bias/fairness checks on key slices tied to business risk.
• Log decisions: why the model was approved and by whom.
• Define a “kill switch” process for high-severity incidents.

10) Which tools map to each checklist area?

Tool choices vary, but your checklist areas are stable.
Treat tooling like interchangeable parts.
Gartner advises leaders to favor platforms with a strong ecosystem rather than stitching many disparate vendors, because execution is hard and consistency matters.
That is especially true when multiple teams ship models. [Source](https://www.gartner.com/en/newsroom/press-releases/2025-05-22-gartner-survey-finds-77-percent-of-engineering-leaders-identify-ai-integration-in-apps-as-a-major-challenge)

11) What’s a step-by-step implementation plan (with timelines)?

Use a 6-week sprint plan to get a real checklist into production.
This works because it forces you to automate early.
McKinsey reports organizations commonly take 1–4 months from start to production for gen AI projects.
A 6-week plan is aggressive but realistic for a first “golden path” pipeline. [Source](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-2024)

Week 1: Define gates and owners

1. Pick one production model as the pilot.
2. Define success metrics + latency budgets.
3. Write a one-page risk list and decide what to monitor.

Week 2: Data validation and dataset versioning

1. Implement schema + missingness checks.
2. Snapshot training data and store lineage.
3. Fail training if data gate fails.

Week 3: Reproducible training

1. Containerize training.
2. Write run manifests.
3. Track metrics and artifacts.

Week 4: CI pipeline

1. Unit tests for feature code.
2. Training smoke test.
3. Build and scan containers.

Week 5: CD + safe deployment

1. Deploy to staging.
2. Canary release plan.
3. Rollback procedure and rehearsal.

Week 6: Monitoring + runbooks

1. Dashboards for drift + latency.
2. Alert thresholds tuned to reduce noise.
3. On-call runbooks linked in every alert.

12) What trends shape MLOps in 2025–2026?

Expect consolidation into “AI application platforms,” not just MLOps point tools.
Gartner estimates the AI application development platforms market at $5.2B and recommends stronger ecosystems for scaling and consistency.
This affects how you buy or build MLOps foundations in 2025 and beyond. [Source](https://www.gartner.com/en/newsroom/press-releases/2025-05-22-gartner-survey-finds-77-percent-of-engineering-leaders-identify-ai-integration-in-apps-as-a-major-challenge)

Expect investment pressure too.
Fortune Business Insights projects the MLOps market to grow from $2.33B in 2025 to $19.55B by 2032.
That market signal usually correlates with more tooling, more best practices, and higher executive expectations for production reliability. [Source](https://www.fortunebusinessinsights.com/mlops-market-108986)

FAQ: Real questions teams ask about an MLOps checklist

Q1) What is the minimum MLOps checklist for a small team?

Start with four gates: data validation, reproducible training, canary deployment, and monitoring for drift + latency.
McKinsey’s survey shows gen AI use is widespread, which means “ship fast” pressure is real, even for small teams.
These four gates protect you without heavy process. [Source](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-2024)

Q2) How often should we retrain models?

Retrain when triggers fire, not just on a calendar.
Google lists retraining triggers including new data availability, significant distribution changes, or performance degradation.
Use a schedule only when your labels arrive predictably. [Source](https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning)

Q3) What’s the difference between deploying a model and deploying an ML system?

Deploying a model is shipping an artifact behind an API.
Deploying an ML system includes the whole pipeline: data validation, training, evaluation, registry promotion, and monitoring.
Google’s MLOps reference explicitly frames CD as delivering a training pipeline that deploys a prediction service. [Source](https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning)

Q4) How do we justify investing in MLOps to leadership?

Use risk and adoption data.
McKinsey reports 44% of respondents experienced at least one negative consequence from gen AI use.
Gartner reports 77% of engineering leaders see AI integration in apps as a pain point.
Your checklist reduces both risk and engineering friction. [Source](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-2024) [Source](https://www.gartner.com/en/newsroom/press-releases/2025-05-22-gartner-survey-finds-77-percent-of-engineering-leaders-identify-ai-integration-in-apps-as-a-major-challenge)

Q5) How do we align MLOps with responsible AI expectations?

Make responsible AI a build gate, not a policy PDF.
HBR describes organizations integrating principles into operations early to avoid disruptive changes later.
Translate that into required checks (privacy, documentation, review sign-off) before production promotion. [Source](https://hbr.org/2024/05/how-to-implement-ai-responsibly)

Sources (complete citations)

• McKinsey & Company (May 30, 2024). “The state of AI in early 2024: Gen AI adoption spikes and starts to generate value.” https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-2024
• Google Cloud Architecture Center (last reviewed Aug 28, 2024). “MLOps: Continuous delivery and automation pipelines in machine learning.” https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
• Gartner (May 22, 2025). “Gartner Survey Finds 77% of Engineering Leaders Identify AI Integration in Apps as a Major Challenge.” https://www.gartner.com/en/newsroom/press-releases/2025-05-22-gartner-survey-finds-77-percent-of-engineering-leaders-identify-ai-integration-in-apps-as-a-major-challenge
• Fortune Business Insights (market report page). “MLOps Market Size, Share & Forecast.” https://www.fortunebusinessinsights.com/mlops-market-108986
• Harvard Business Review (May 10, 2024). “How to Implement AI — Responsibly.” https://hbr.org/2024/05/how-to-implement-ai-responsibly
• ML-Ops.org. “MLOps Principles.” https://ml-ops.org/content/mlops-principles
• YouTube (course). “Ultimate MLOps Full Course in One Video.” https://www.youtube.com/watch?v=w71RHxAWxaM
• ProjectPro (image reference). “Understanding MLOps Lifecycle.” https://www.projectpro.io/article/mlops-lifecycle/885

Conclusion: Next steps you can do this week

If you want a checklist that actually ships models, treat it as automation, not documentation.
Start with one pilot model and implement four gates in 7 days: data validation, reproducible training, canary deploy, and monitoring.
Then expand to CI/CD maturity over 6 weeks using the plan above.
This aligns with the reality that AI adoption and AI-related risks are already mainstream. [Source](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-2024)