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A production-grade MLOps pipeline is the repeatable system around a model: it prepares and validates data, trains and evaluates candidates, versions and releases approved artifacts, serves predictions, and monitors what happens in production. Build it as an iterative lifecycle with explicit quality gates and ownership—not as a training script that happens to run on a schedule.
How do I build an end-to-end MLOps pipeline from scratch?
Start by defining the prediction use case and its operating constraints: what decision the model supports, what counts as an acceptable result, how fresh predictions must be, and what risks require a human review or fallback. Those choices determine data requirements, evaluation criteria, serving design, monitoring, and release controls. There is no single architecture or compute profile that fits every project.
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Google Cloud’s MLOps guidance describes the central challenge as: “The real challenge isn’t building an ML model, the challenge is building an integrated ML system and to continuously operate it in production.” Its documentation, last reviewed on August 28, 2024, focuses primarily on predictive AI systems. The lifecycle below is a useful starting point for that class of system; LLM applications can require additional operational controls that this predictive-ML architecture does not specify.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitches- Define the contract. Document the prediction target, input and output schemas, intended users, quality measures, latency and availability needs, data access rules, and what the service should do when inputs are invalid or unavailable. Set task-specific acceptance criteria before comparing models.
- Build data preparation. Ingest source data, validate it, engineer features, and assemble a reproducible training dataset. Record enough information to identify the data and transformations used for each run.
- Make training reproducible. Keep modeling code, configuration, parameters, and dependencies under version control. Track experiments and retain the trained artifact and run metadata so a candidate can be evaluated and traced.
- Evaluate before release. Test data and pipeline components as well as model quality. Define checks for the actual task; a successful software build or passing unit tests do not show that the resulting model is fit for production.
- Register and promote a candidate. Record candidate versions and lineage, review evaluation results, and move only an approved artifact through your release stages.
- Serve and observe. Package the model with its dependencies, metadata, and inference schema. Monitor service health and model- or data-related behavior, then use what you learn to inform the next pipeline run or experiment.
Kubeflow’s architecture describes a lifecycle that also includes optimization between training and serving. That stage can be useful for hyperparameter tuning or model optimization; it does not mean every project needs distributed training or AutoML.
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Turn the lifecycle into a deployable design
A practical first implementation can separate responsibilities into a version-controlled pipeline definition, reusable data and model components, a run store for metadata and artifacts, a registry or approval mechanism, and a serving target. Connect these pieces with automation, but keep the release decision explicit: a pipeline run may produce a candidate without making that candidate the production model.
Before selecting infrastructure, write down the system’s requirements and constraints. Training workload and inference workload may have different compute needs. Serving may run locally, in a cloud service, or on Kubernetes; the right choice depends on the use case, latency and availability expectations, security and governance requirements, team capabilities, and budget.
What belongs in a production ML pipeline besides training?
Training is one stage in a broader operating system. Google Cloud’s MLOps guidance calls out configuration, automation, data collection and verification, testing and debugging, resource management, model analysis, process and metadata management, serving infrastructure, and monitoring. A useful implementation makes each responsibility visible rather than hiding it inside one opaque training job.
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Data and pipeline checks
Validate input schemas, expected ranges, missingness, and consistency between training and serving data. These examples are implementation recommendations; Google Cloud identifies data validation as an ML-specific testing category but does not prescribe universal thresholds. Test individual components and their integrations so that data preparation, feature engineering, and orchestration failures are caught before a candidate reaches release.
Model quality and release evidence
Evaluate each trained candidate against task-specific criteria, and retain the results with the artifact and run metadata. Model quality checks are distinct from unit and integration tests: the software can behave as written while the model remains unsuitable for the intended decision. Use a review or approval step where the impact of a bad release warrants it.
Traceability and packaging
For each candidate, preserve links between the code, data, parameters, metrics, artifacts, and model version. MLflow documents experiment tracking and model lineage/versioning capabilities. Package dependencies and the inference schema with the model to reduce the chance that the serving environment interprets or loads it differently from the environment in which it was evaluated.
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Service and model monitoring
Monitor service behavior, such as whether predictions are being served as expected, alongside data summaries and online model performance where those signals are available. A change in the data profile can reduce model performance without a code defect. Define who receives alerts and what action follows: investigation, traffic rollback, a new training run, or a decision to leave the current model in place.
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They automate different changes. CI and CD move changes to the pipeline implementation; continuous training runs that implementation to create a new model. Model delivery is a further step: making an accepted trained model available as a prediction service.
| Process | What changes or runs | Typical reason to run it |
|---|---|---|
| Continuous integration (CI) | Builds and tests pipeline code and components. | A code or component change; checks can include unit tests for feature engineering. |
| Continuous delivery or deployment (CD) for the pipeline | Deploys a changed, approved pipeline implementation to its target environment. | A pipeline implementation has passed the required checks and approvals. |
| Continuous training (CT) | Executes the deployed pipeline to train a model candidate. | New data, a schedule, an on-demand request, or another configured trigger. |
| Model delivery | Makes an accepted trained model available through a prediction service. | A candidate has passed the release criteria and is approved for serving. |
| Monitoring | Observes live service, data, and model behavior. | Runs continuously after deployment and can prompt investigation or another pipeline run. |
Google Cloud’s TFX guidance separates pipeline deployment from training execution: a new pipeline implementation can be deployed independently of a run that trains a new model. This separation makes it possible to change orchestration or validation code without automatically replacing the live model, and to train a candidate without automatically releasing it.
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A practical change path
- When pipeline code changes, run CI checks on the code and components, including relevant integration and data-validation tests.
- After the implementation is approved, use CD to deploy that pipeline version to its target environment.
- Trigger CT according to the project’s policy. The run should create a candidate and its evaluation evidence, not bypass release gates.
- Review the candidate against the established criteria. Promote and serve it only through the model-release process.
- Monitor the live system. Treat alerts as signals for investigation or action, not automatic proof that retraining is always the right remedy.
How do I know when a production model should be retrained?
Set triggers based on the data and risk of the specific application. Google Cloud’s TFX reference describes possible triggers including an on-demand request, a schedule, new data, degraded model performance, or significant changes in data statistics. These are design options, not a universal retraining policy.
Choose triggers and thresholds deliberately
For each trigger, specify what is measured, the threshold or condition that warrants action, the time window or data volume needed to interpret it, and who or what initiates the next step. For example, a change in input statistics may be a reason to investigate, but not necessarily to replace the model: the change could reflect a harmless seasonal shift, a source-system defect, or a meaningful change in the population the model serves.
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Make retraining safe to operate
- Keep the current approved model available while a new candidate is trained and evaluated.
- Require a candidate to pass the same relevant quality and validation gates as other releases.
- Set alert ownership and an investigation path for unexpected data or performance changes.
- Define rollback conditions in terms of measured behavior and business impact; no cited guidance supplies thresholds that fit every task.
Should I use MLflow or Kubeflow?
Compare the work your team needs to operate, rather than treating the products as universal substitutes. Their documented emphases overlap in parts of the lifecycle, but differ in framing and deployment context; a team may use orchestration and experiment or registry tools together.
| Decision axis | MLflow | Kubeflow |
|---|---|---|
| Documented emphasis | Experiment tracking, evaluation, model registry, versioning, deployment, and monitoring. | Modular Kubernetes-native components across data preparation, development, training, optimization, registry/artifacts, and pipelines. |
| Deployment context | Documents local, cloud, and Kubernetes serving targets, with models packaged with dependencies and metadata. | Built on Kubernetes; can be used as a distribution or through independently usable subprojects. |
| Questions to resolve | Which lifecycle functions, registry needs, serving destination, and existing-stack integrations are required? | Does the team have the Kubernetes operations capability, orchestration scope, workload needs, and desire for composable lifecycle components? |
Choose based on required lifecycle functions, team skills, data volume, latency and availability needs, security, governance, and budget. The product documentation describes capabilities, not a benchmark establishing that one choice is superior for every project. MLflow’s serving documentation names local, cloud, Kubernetes, Amazon SageMaker, and Databricks Model Serving as deployment options; that list is not a claim that every option is available in every region or suitable for every workload.
What should the first production release prove?
Before promoting a model, make sure the release record answers the operational questions that will matter if the model behaves unexpectedly:
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- Which code, data, configuration, dependencies, and model version produced the candidate?
- Which data, component, integration, and model-quality checks passed, and which release criteria were applied?
- What inference schema and serving package are expected by the prediction service?
- Who approved promotion, and what is the procedure for investigating an alert or rolling back a release?
- Which data or performance conditions can trigger a new run, and which require investigation before retraining?
These controls are a recommended implementation pattern, not a single tested reference deployment. The cited guidance provides lifecycle patterns and tool capabilities; infrastructure, thresholds, and release policy must be tailored to the application.
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