Checklist · Data and ML
ML Model Deployment Checklist
Opens with creditsA checklist for taking a trained machine learning model into production and keeping it healthy there. It covers baselines and metrics, training and serving consistency, offline validation, packaging and the model registry, staged rollout and rollback, monitoring for skew and drift, and ownership, with a severity and a verification step for each check.
Written by Tauseef Fayyaz7 chapters, 30 checksAbout 14 minReviewed 13 October 2026What you will learn
7 chapters · about 14 min
1Baselines, metrics and scopeDecide what the model must beat and how you will measure it before you deploy anything. These checks set the bar the rest of the list measures against.4 checks · 2 min
- 1Have a simple baseline, such as a heuristic or the current system, to compare against
- 2Define and instrument the product and model metrics before launch
- 3Ship a simple first model and get the pipeline and serving right
- 4Score the system against a production readiness rubric
4 in this chapterOpen this chapter2Features and training-serving consistencyA model only performs in production as well as the features it receives there. These checks keep training and serving features the same.5 checks · 2 min
- 1Use the same feature code in training and serving
- 2Log the features used at serving time
- 3Snapshot or version lookup tables that features are joined from
- 4Validate input data schema and value ranges before training and at serving
- 5Give every feature an owner and a description
5 in this chapterOpen this chapter- Offline validation before releaseOffline checks are the cheapest place to stop a bad model. These checks compare the candidate with the live model on data that looks like production.4 checks · 2 minOpens with credits
- Packaging, registry and reproducibilityEvery model in production should be traceable to its code, data and run. These checks make a model version something you can inspect, reproduce and roll back.5 checks · 2 minOpens with credits
- Rollout and rollbackOffline wins do not always survive real traffic. These checks expose a new model to production gradually and make it easy to back out.4 checks · 2 minOpens with credits
- Monitoring, skew and driftModels decay quietly as the world changes. These checks make that decay visible and connect it to retraining.4 checks · 2 minOpens with credits
- Ownership, documentation and governanceModels in production need owners and a record of what they are for. These checks follow the Model Cards paper and NIST's AI Risk Management Framework.4 checks · 2 minOpens with credits
About this checklist
Training a model that scores well in a notebook is the start of the work, not the end. Getting it into production means answering a different set of questions: will it see the same features at serving time that it saw in training, how do you know the new version is better than the one already live, what happens to traffic if it is worse, and who notices when its accuracy slowly decays three months from now. This ML model deployment checklist covers those questions.
The checks draw on Google's Rules of Machine Learning, Google Cloud's guidance on MLOps pipelines, the ML Test Score rubric for production readiness, the MLflow and KServe documentation, the Model Cards paper and NIST's AI Risk Management Framework. Tool names appear where they make a check concrete, but the checks apply whichever registry, serving platform or cloud you use.
One idea from Google's Rules of ML runs through the list: get the infrastructure right before making the model clever. Many production problems are not modelling problems at all. They are features computed differently in two places, a lookup table that changed after training, or a model nobody is watching. The early chapters catch these before launch; the later ones catch them after.
Each check has a severity (must, should or nice) and a concrete way to verify it. Work through the must checks before the first production release, and treat the rest as the plan for the releases that follow.
Common questions
What is training-serving skew?
Training-serving skew is a difference between how a model performs in training and how it performs in production, usually because features are computed differently in the two places or data changed in between. Reuse feature code in both pipelines, log serving features and compare offline and live outputs on the same inputs.
How should I roll out a new ML model?
Validate it offline against the current model on recent data and important segments, then send a small share of traffic to it as a canary or A/B test while watching product and guardrail metrics. Promote it gradually with an approval step, and keep rollback to the previous version a single, rehearsed action.
What should I monitor for a model in production?
Monitor input feature distributions and missing-value rates for drift, compare live and offline predictions for skew, track model quality as ground truth labels arrive, and watch latency and error rates. Alert on thresholds set from history, and route alerts to the model's owner.
What is a model card?
A model card is a short document that accompanies a trained model, describing its intended use and out-of-scope uses, how it was evaluated and how it performs across relevant groups. The idea comes from the Model Cards for Model Reporting paper and helps keep models out of settings they are not suited for.
How often should a model be retrained?
It depends on how fast its performance degrades. Google's Rules of ML recommends measuring freshness needs directly. Retrain on a schedule or on triggers such as new data, drift or a drop in live quality, and send every retrained model through the same validation and rollout.
Sources (8)
Checked against these, last on 13 October 2026.
- Rules of Machine Learning: Best Practices for ML Engineering, Google for Developersdevelopers.google.com/machine-learning/guides/rules-of-ml
- MLOps: Continuous delivery and automation pipelines in machine learning, Google Clouddocs.cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
- The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction, Google Researchresearch.google/pubs/the-ml-test-score-a-rubric-for-ml-production-readiness-and-technical-debt-reduction/
- MLflow Model Registry, MLflow docsmlflow.org/docs/latest/ml/model-registry/
- Model Signatures and Input Examples, MLflow docsmlflow.org/docs/latest/ml/model/signatures/
- Canary rollout strategy, KServe docskserve.github.io/website/docs/model-serving/predictive-inference/rollout-strategies/canary
- Model Cards for Model Reporting, arXivarxiv.org/abs/1810.03993
- AI RMF Core, NIST AIRCairc.nist.gov/airmf-resources/airmf/5-sec-core/
Related on Sefism
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Roadmaps
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See allBest practices
See allProject structures
See all- MLOps pipeline project structureFor engineers who can train a model and now need the training to be a pipeline anyone can rerun, review and release. A uv workspace holds the pipeline package (prepare, validate, featurize, train, evaluate, register steps run by DVC), a small shared feature package, and a separate FastAPI prediction service that loads models from the MLflow registry by alias. It grows into CI that reproduces the pipeline on pull requests, scheduled retraining, approved promotion and monitoring.
- Machine learning project project structureFor students and analysts whose model works in a notebook and now needs to be reproducible, reviewable, served and watched. It starts from the Cookiecutter Data Science v2 layout (immutable raw data, numbered notebooks, a real Python package) and grows into versioned data with DVC, typed parameters, data validation with pandera, experiment tracking and a model registry with MLflow, a FastAPI prediction service, drift monitoring and, for a team, scheduled retraining, model cards, fairness checks and shadow deployment.
- Apache Airflow project structureFor data engineers scheduling real pipelines with Airflow 3. It starts from
astro dev init(dags, include, plugins, tests and a Runtime Dockerfile that runs Airflow locally in Docker) and grows into dags grouped by business area, logic in a tested include package, asset-driven scheduling, data quality checks, alerts, CI that parses and tests every dag, and dag-only deploys. A self-hosted docker compose and Helm route is shown alongside.
Career questions
See all- My machine learning projects never leave a Jupyter notebook. How do I learn to deploy a model?Move the notebook code into a small Python package, save the whole preprocessing and model pipeline, put it behind a FastAPI endpoint in a Docker container and deploy it. Then add what makes it production: tracked runs, a registry, a test in CI, prediction logging and a drift check. Start with batch predictions if you can.
- What is the difference between a data analyst, a data scientist and a machine learning engineer?Analysts answer business questions with SQL, spreadsheets, dashboards and reports. Data scientists add statistics, experiments and predictive models. ML engineers build and run models inside production software. Titles vary by company, so read the work in the job advert, and pick by the kind of problem you enjoy, not the title that sounds most senior.
- How do I become an MLOps engineer?MLOps engineers keep models working in production: packaging, versioning, pipelines, serving, CI/CD and monitoring. Come in from either software or DevOps with ML basics, or from ML with engineering habits. Learn the lifecycle in order, then prove it with one model you deploy, monitor and roll back yourself.
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