A DevOps team inherits a production ML system. The CI/CD pipeline builds and ships it like any other service. Six months later, model accuracy has quietly slid, and nobody can say when it started or why — because the monitoring stack was never built to watch for it. The rollback procedure assumes a deterministic build, so rolling back the code doesn’t restore the behaviour. This is the moment a lot of teams discover that “we already have DevOps, ML deployment is just another service” was a more expensive assumption than it looked. The honest framing is this: MLOps is a deliberate extension of DevOps, not a parallel discipline that replaces it. Most of your existing investment carries over. A meaningful slice does not — and the slice that doesn’t is precisely the part that determines whether a model stays healthy in production. The mistake isn’t adopting DevOps practices for ML. The mistake is assuming they cover the whole surface. If the term “MLOps” itself is still fuzzy, the companion piece on MLOps as the operating model that keeps production machine learning healthy grounds the vocabulary this comparison leans on. This article assumes you have a working mental model of both and want to know exactly where they diverge. What’s the Actual Difference Between MLOps and DevOps in Day-to-Day Work? DevOps governs software whose behaviour is fully specified by its source code. Given the same inputs, the same build produces the same outputs. That determinism is the quiet foundation underneath almost every DevOps practice: versioning, testing, rollback, and reproducibility all assume that the code is the system. Machine learning breaks that assumption. A trained model’s behaviour is determined by code and by the data it learned from and by the data it now sees in production. You can have a byte-identical binary that behaves worse this month than last, because the world the model is predicting against shifted underneath it. Nothing in the deployed artefact changed. The environment did. That single property — behaviour is a function of data, not just code — is where the two operating models diverge, and almost every concrete difference downstream traces back to it. DevOps optimises for shipping deterministic code reliably. MLOps has to additionally manage the non-determinism that data introduces: versioning datasets, tracking which model came from which experiment, and watching production inputs for drift that no unit test would ever catch. Which DevOps Practices Carry Over, and Which Need Extending? The good news for any team already running mature DevOps: most of the machinery is reusable. In our experience advising teams through this transition, the reuse rate is high enough that “rip and replace” is almost always the wrong instinct — you extend, you don’t restart. Roughly 60–70% of existing CI/CD, observability, and infrastructure-as-code investment transfers directly, with the remaining 30–40% needing genuinely new artefacts (this is an observed planning heuristic from our engagements, not a benchmarked figure — the exact split depends on how data-heavy the system is). The cleanest way to see the boundary is to walk practice by practice. MLOps vs DevOps: What Transfers and What Needs New Artefacts Practice DevOps version MLOps status What changes CI/CD Build, test, ship code Extend Pipeline must also retrain, validate model quality, and gate on accuracy thresholds — not just unit tests passing Version control Git on source Extend Code versioning carries over; you add dataset, feature, and model-artefact versioning that Git was never designed for Infrastructure as Code Terraform / Ansible for infra Reuse GPU provisioning, training clusters, serving infra — same IaC tooling, ML-specific resources Observability Logs, metrics, traces, uptime Extend Service-level telemetry transfers; you add data-drift, prediction-distribution, and accuracy monitoring that has no DevOps equivalent Rollback Redeploy previous build Extend Code rollback works; you must also be able to roll back to a prior model + the dataset/feature state it expects On-call / incident Service down, error spike Extend Outage playbooks transfer; “silent degradation” is a new incident class with no error and no alert by default Artefact registry Container / package registry Reuse + new Containers stay; you add a model registry linking each model to its training run, data, and metrics The pattern in that table is consistent. Reuse is genuine — IaC for GPU clusters is still IaC, and a model served behind an API is still a service that needs uptime monitoring. The extensions cluster around one theme: anything that has to reason about data or model behaviour, rather than code, is where DevOps tooling runs out. What New Artefacts Does MLOps Add That DevOps Never Tracked? DevOps versions code and configuration. That’s the universe it was built for. MLOps adds four artefact classes that DevOps tooling has no native concept of, and each one introduces a reproducibility requirement that Git and a container registry can’t satisfy alone. Datasets. The training set is part of the system’s definition. Two models trained on the same code but different data snapshots are different systems. Tools like DVC or lakeFS exist specifically because Git chokes on multi-gigabyte data versioning, and you need to be able to answer “which exact data produced this model?” months later. Features. When the same feature transformation runs at training time and serving time, any drift between the two — a subtle difference in how a value is computed offline versus online — produces training-serving skew, a failure mode with no analogue in conventional software. Feature stores like Feast exist to make the offline and online paths share one definition. Models. A model artefact is not just a binary. It’s a binary plus the experiment that produced it, the data it saw, the hyperparameters, and the validation metrics. A model registry (MLflow is the common reference point) links all of that so a production model is traceable back to its lineage — which is exactly what your rollback procedure needs and what a container registry cannot give you. Experiment runs. ML development is empirical: you try many configurations and keep the best. Tracking which run produced which result, with which data, is a first-class concern that has no DevOps equivalent. Without it, “reproduce last quarter’s best model” becomes archaeology. The recurring cost of not drawing this line cleanly is tool fragmentation: a model degradation episode the team can’t even diagnose, conflicting ownership between the DevOps and ML sides, and a months-long argument every time a model needs to ship. Naming the four artefact classes up front is what lets a team budget for the right extensions instead of discovering them under incident pressure. How Do MLOps and DevOps Teams Collaborate When a Model Ships Inside an App? Most ML doesn’t ship standalone. It ships as a component inside a larger application that a DevOps team already operates. The collaboration question is therefore not “who owns deployment” but “where is the seam, and who owns each side of it.” A workable division we see hold up in practice: the DevOps team owns the application’s CI/CD, the serving infrastructure, and the service-level observability — uptime, latency, error rates. The ML side owns the model lifecycle behind the serving boundary — retraining triggers, accuracy gates, drift monitoring, and the model registry. The model artefact is the contract between them: DevOps treats it as a versioned dependency it deploys; MLOps treats it as the output of a lifecycle it manages. This works because it respects the determinism boundary. Everything on the DevOps side is deterministic and fits existing practices. Everything on the ML side is data-dependent and needs the extensions. The seam fails when a team tries to push drift monitoring or accuracy gating onto a DevOps observability stack that has no concept of either — which is the silent-degradation trap from the opening, restated as an org-design problem. How Do MLOps and AIOps Differ — and Where Does Each Sit Relative to DevOps? These three get conflated because they share a suffix and overlap in tooling, but they answer different questions: DevOps — how do we reliably ship and operate software? MLOps — how do we reliably ship and operate machine learning systems, where data shapes behaviour? AIOps — how do we use ML to operate IT systems (anomaly detection on logs, automated incident triage)? The trap is treating them as a progression. They’re not a ladder. MLOps extends DevOps for ML workloads; AIOps applies ML to operations. A team can run AIOps tooling to triage incidents while having no MLOps maturity at all, and vice versa. When someone asks “should we do MLOps or AIOps,” the answer is usually that they’re solving two unrelated problems and the question conflates them. Can a DevOps Team Adopt MLOps Incrementally — and What’s the First Step? Yes, and incrementally is almost always the right path. Requiring a new platform from day one is the over-correction to the “DevOps covers it” under-correction; both are expensive. Because 60–70% of the tooling transfers, the realistic transition is additive: keep your CI/CD, IaC, and serving infrastructure, and layer in the missing artefacts in the order that retires the most risk. The practical first step is rarely a tool purchase. It’s a question: is this an ML system masquerading as a software project? If a system’s behaviour depends on data that shifts in production, it needs the data-aware extensions whether or not anyone has called it “MLOps” yet. Getting that classification right early is the difference between budgeting for the extensions and discovering them six months in. It’s also, not coincidentally, one of the first things to get wrong in the broader pattern of why most enterprise AI projects fail before they reach production — the diagnosis is frequently an operating-model mismatch dressed up as a technical problem. A defensible incremental sequence, highest-risk-first: Drift and accuracy monitoring — close the silent-degradation gap before anything else; it’s the failure you can’t see coming. Model registry + lineage — make every production model traceable to its data and experiment, which also fixes rollback. Dataset and feature versioning — make models reproducible. Retraining in CI/CD — automate the lifecycle once the safety nets exist. You can stop at any rung and still be better off than the all-code pipeline you started with. The work that matters most — knowing when a model is degrading — sits at step one, not at the end. FAQ What’s the actual difference between MLOps and DevOps in day-to-day work? DevOps governs software whose behaviour is fully determined by its source code, so the same build always behaves the same way. MLOps has to manage systems whose behaviour also depends on training data and on live production inputs, which means a byte-identical model can degrade without any code change. Day to day, that adds dataset versioning, model lineage tracking, and drift monitoring on top of standard DevOps work. Why does machine learning need its own operating discipline rather than fitting into existing DevOps pipelines? Because ML behaviour is a function of data, not just code, and DevOps practices — versioning, testing, rollback, monitoring — all assume the code is the system. A standard CI/CD pipeline can build and ship a model, but it has no concept of accuracy gates, training-serving skew, or silent data drift. Those gaps are exactly where models fail in production, so they need first-class handling rather than a workaround. Which DevOps practices apply directly to ML, and which need to be extended? Infrastructure as Code and artefact registries largely transfer as-is — GPU provisioning is still IaC, and a served model still needs uptime monitoring. CI/CD, version control, observability, rollback, and on-call all need extending: the pipeline must validate model quality, version control must track datasets and models, and observability must add drift and accuracy signals that have no DevOps equivalent. What new artefacts does MLOps add that DevOps was not designed to version or track? Four: datasets, features, models, and experiment runs. DevOps versions code and configuration; it has no native concept of which data snapshot produced a model, how to keep offline and online feature definitions in sync, or how to link a model artefact back to its training run and metrics. Tools like DVC, Feast, and MLflow exist specifically to fill these gaps. How do MLOps and DevOps teams collaborate when an ML model ships inside a larger application? The model artefact becomes the contract between them: the DevOps team owns application CI/CD, serving infrastructure, and service-level observability, while the ML side owns the model lifecycle — retraining, accuracy gates, drift monitoring, and the registry. This works because everything DevOps owns is deterministic and fits existing practice, while everything behind the serving boundary is data-dependent and needs the MLOps extensions. Can a DevOps team adopt MLOps incrementally, or does it require a new platform from day one? It can and usually should be incremental, because roughly 60–70% of existing CI/CD, IaC, and observability tooling transfers directly (an observed planning heuristic from our engagements, not a benchmarked rate). The transition is additive: layer in drift monitoring first, then model lineage, then dataset and feature versioning, then automated retraining. A new platform from day one is the over-correction to assuming DevOps covers everything. How do MLOps and AIOps differ, and where does each fit relative to DevOps? DevOps reliably ships and operates software; MLOps extends DevOps to operate ML systems where data shapes behaviour; AIOps uses ML to operate IT systems, such as anomaly detection on logs. They aren’t a progression — a team can run AIOps for incident triage with zero MLOps maturity, and vice versa. Conflating them usually means two unrelated problems are being treated as one. If a team already has DevOps, what’s the practical first step in transitioning to MLOps? The first step isn’t a tool purchase — it’s a classification question: is this an ML system masquerading as a software project? If behaviour depends on data that shifts in production, it needs data-aware extensions regardless of labels. The highest-value concrete move is adding drift and accuracy monitoring to close the silent-degradation gap before automating anything else. Where This Leaves a Team Standing at the Decision The question that actually matters isn’t “MLOps or DevOps” — it’s whether the system in front of you derives its behaviour from code alone or from code plus shifting data. Get that classification right and the rest follows: you reuse what transfers, extend what doesn’t, and budget for the four artefact classes before they surface as an incident. Get it wrong and you inherit the opening scenario, six months in, with a model degrading and no instrument pointed at it. If you’re sitting at exactly that decision — a working DevOps practice and an ML system you’re not sure it covers — the cleanest next move is to pressure-test the classification before committing budget. Our services and the underlying technologies we work with are built around that distinction, and an early AI project risk assessment exists precisely to answer the “is this an ML system masquerading as a software project?” question before it answers itself the expensive way.
