DevOps vs MLOps: What Changes When You Ship Models?

DevOps taught software teams to automate building, testing, deploying, and operating code. Machine learning systems inherit all of that and add a complication: their behavior comes from data and trained models as well as code, which change on different schedules and fail in different ways. MLOps is the set of practices that extends DevOps to cover them. This comparison covers what changes and what stays, drawing on FISTA Solutions' AI enablement practice. Foundations are in what is mlops and the pipeline build in how to build a ci-cd pipeline for machine learning.

What is DevOps?

DevOps is the practice of integrating development and operations through automation and shared ownership: version control, continuous integration with automated tests, continuous delivery and deployment, infrastructure as code, observability, and incident response. Its artifacts are code and configuration; its tests are deterministic; its deployments are reproducible from source; and its monitoring watches availability, latency, and errors.

What is MLOps?

MLOps extends those practices to systems containing trained models. It adds versioning of datasets and features alongside code, experiment tracking to record what was tried, a model registry as the system of record for versions and approvals, statistical evaluation as release gates, deployment patterns suited to models such as shadow and canary, monitoring for data and prediction drift and quality decay, and retraining pipelines. Its artifacts include data and model weights; its tests are statistical; and its monitoring watches behavior, not only health. Registry design is in how to build a model registry.

How do they compare?

Dimension	DevOps	MLOps
What defines behavior	Code	Code, data, and model weights
Versioned artifacts	Code, config	Plus datasets, features, experiments, models
Testing	Deterministic, binary	Plus statistical quality gates on evaluation sets
Build step	Compile and package	Plus training and evaluation runs
System of record for releases	Artifact repository	Plus model registry with approvals
Deployment patterns	Blue-green, canary	Plus shadow deployment and model comparison
Monitoring	Availability, latency, errors	Plus data drift, prediction drift, quality decay
Trigger for change	Code commit	Code commit, data change, drift, scheduled retraining
Roles	Developers, platform engineers	Plus ML engineers, data engineers, data owners
Governance	Change management	Plus model risk, lineage, documentation

What changes in versioning?

DevOps reproduces a release from a code commit. MLOps must reproduce a model from code plus the exact data and parameters that trained it, so datasets and features are versioned, experiments are tracked with their inputs, and models carry lineage back to both. Without this, a production model cannot be explained or rebuilt. Feature infrastructure is in how to build a feature store and lineage in what is data lineage in ai.

What changes in testing and release gates?

Software tests pass or fail. Model quality is a distribution, so pipelines evaluate candidates on held-out and golden datasets against thresholds, compare against the current production model, check fairness and robustness where relevant, and gate promotion on results. Approval workflows in the registry record who accepted what evidence. Gate design is in how to build an ai quality gate.

What changes in deployment?

Models are served as versioned artifacts behind stable interfaces, promoted through registry stages, and released with patterns that reveal behavioral differences: shadow deployments that score live traffic without acting, canaries that compare outcomes on a slice, and quick rollback to prior versions. Patterns are in what is a shadow deployment and what is a canary deployment.

What changes in monitoring?

Health monitoring remains, and behavioral monitoring is added: input data drift against training distributions, prediction drift, quality decay measured against delayed ground truth, and business outcome tracking. Alerts trigger investigation and, where appropriate, retraining. Drift concepts are in what is model drift and monitoring design in how to build a real-time ai monitoring system.

How do teams and roles change?

Platform engineers continue to own infrastructure and pipelines; machine learning engineers own training, evaluation design, and retraining decisions; data engineers own data pipelines and quality; and data owners participate in release decisions. The release process includes evidence review rather than test pass alone. Team design is in ai team structure and hiring in hire mlops engineers and hire devops engineers.

How do the platforms fit together?

MLOps runs on DevOps infrastructure: the same version control, CI runners, container platforms, infrastructure as code, and observability stack, with ML-specific services added: experiment tracking, feature store, registry, evaluation runners, and drift monitoring. Building MLOps as a separate island duplicates effort and weakens governance. Tooling choices are in mlflow vs weights and biases and platform placement in kubernetes vs serverless for ml.

What does the transition look like in practice?

A company with mature DevOps adds dataset versioning and experiment tracking to its repositories, stands up a registry integrated with its CI, adds evaluation gates to its pipelines, deploys models behind its existing service mesh with shadow and canary support, and extends its observability stack with drift dashboards. ML engineers join release reviews. The cost is incremental; the alternative is models in production that nobody can reproduce or trust. Platform cost is in mlops platform cost.

How FISTA Solutions builds MLOps on DevOps

FISTA Solutions extends clients' existing DevOps platforms rather than building parallel ones, adding versioning, registries, statistical gates, model deployment patterns, and drift monitoring on the same infrastructure, and pairing platform engineers with machine learning engineers in one release process. The AI enablement practice delivers the platform, AI agents and custom models ship through it, and forward deployed engineers embed with client platform teams. The record behind the approach is 150+ projects with 99.9% uptime.

To extend a DevOps platform for models, message FISTA on WhatsApp, or read llmops vs mlops for what changes again with large language models.