MLOps is not DevOps with a different name MLOps borrows concepts from DevOps — automation, reproducibility, monitoring — but applies them to a fundamentally different type of software artifact: a trained model. Models have properties that regular software does not. They degrade silently when input distributions change, their “code” (weights) cannot be version-controlled the way a Python file can, and retraining means re-running an expensive compute process rather than recompiling. This makes most of the DevOps reflexes — pin the version, deploy, monitor uptime — only partly useful. Understanding what MLOps addresses, and what it doesn’t, is a prerequisite to deciding whether and how to invest in it. The honest framing is narrower than most vendor pitches: MLOps is the set of practices that close the gap between a model that works on a data scientist’s laptop and a model that keeps working in production six months later. What does model deployment look like without MLOps? Without MLOps practices, ML teams hit a recognisable set of problems: A model performs well in development but behaves differently in production because the training and serving environments differ (library versions, data preprocessing steps, random seeds). A model degrades in production over six months as the input data distribution shifts, but no one notices until users complain. A data scientist trains a new version of a model, but there is no safe way to deploy it without taking the current version offline. The team wants to retrain on new data but cannot reproduce the original training run to verify the new version is actually better. These are not exotic failures. They are the default state of an ML project that grew out of a notebook and was deployed by hand. They are also the reason most organisations that build models never put them into production — the path from notebook to running service crosses several unsolved engineering problems at once. What does MLOps actually provide? Problem MLOps solution Irreproducible training Experiment tracking (MLflow, Weights & Biases), versioned data, pinned environments Silent degradation Data drift monitoring, model performance monitoring in production Risky deployments Canary deployments, A/B testing, rollback capabilities Manual retraining Triggered or scheduled retraining pipelines Model versioning Model registry with lineage tracking Environment inconsistency Containerised training and serving (Docker, Kubernetes) Each row in that table is a discipline, not a checkbox. Adopting MLflow as a registry without changing how experiments are logged buys very little; adopting drift monitoring without deciding what action a drift alert should trigger generates noise. The tooling is the easy part. When do you actually need MLOps? MLOps investment is appropriate when: You have models in production that real business processes depend on. Model degradation would be noticed late — after business impact, not before. Retraining is required more than once a year. More than one person works on the same model. MLOps is overhead when: You are in a proof-of-concept phase with no production models. Models are trained once and are essentially static (rare-update batch scoring). The team is one person working alone. The business impact of model failure is low. In our experience across MLOps engagements, organisations adopt MLOps tooling either too early — before they have a production model to maintain — or too late, after a series of painful production incidents. The right time is when you are actively deploying your first model to production. That is when each piece of infrastructure has a concrete problem to solve, which is the only condition under which a team will actually learn to use it. For more on how MLOps applies specifically to organisations that have never deployed a model before, MLOps for organisations that have never operationalised a model covers the starting point in detail. What does MLOps maturity look like at different stages? MLOps maturity progresses through four stages, and organisations benefit from understanding which stage they are at before investing in advanced tooling. Stage 1 — Manual. Data scientists train models in notebooks, export model files manually, and hand them to engineers for deployment. Deployment is a manual process involving SSH, file copying, and service restarts. Retraining happens when someone remembers to do it. This stage works for one or two models but does not scale. Stage 2 — Automated training. Training pipelines are automated: data is extracted, transformed, and used to train models on a schedule. Model artifacts are stored in a registry with version tracking. Deployment remains manual — an engineer reviews the trained model, approves it, and triggers a deployment script. This stage typically supports 5–10 models with modest operational effort (observed pattern across our engagements; not a benchmarked rate). Stage 3 — Automated deployment. The full pipeline from data ingestion through model training to model deployment is automated, with quality gates at each stage. New model versions deploy automatically if they pass quality checks. Monitoring detects model performance degradation and triggers retraining. This stage supports 10–50 models with a small MLOps team — typically two to four engineers, again as an observed planning range rather than an industry benchmark. Stage 4 — Self-managing. The system manages model lifecycle decisions: when to retrain, which features to include, how to allocate compute resources across models, and when to retire underperforming models. Human oversight becomes strategic — setting policies, reviewing aggregate metrics — rather than operational. Few organisations reach this stage, and it is only justified for environments with hundreds of models. We assess clients at their current maturity stage and recommend investments that advance them one stage — not two. Jumping from Stage 1 to Stage 3 introduces tools and practices the team is not ready to use effectively, resulting in expensive infrastructure that does not deliver its intended value. The shelf life of an unused monitoring stack is short; teams quietly stop looking at the dashboards, and within a quarter the alerts get muted. The business case in concrete terms The case for MLOps becomes clear when you cost out the alternative. In our experience, manual model deployment takes roughly two to four hours of engineer time per deployment once you count the environment reconciliation, the sanity checks, and the inevitable rollback rehearsal. If a model requires weekly retraining — common for models operating on changing data — that is on the order of 100–200 hours of engineering time per year per model on deployment alone, not counting monitoring, debugging, and retraining effort. These figures are planning heuristics from our engagements, not benchmarked rates. At ten models, manual operations consume one to two full-time engineers. MLOps automation reduces per-deployment effort to near zero on the happy path, plus monitoring review time (around half an hour per week), freeing those engineers for higher-value work on new model development and system improvement. The repeatable infrastructure has a second-order effect that matters more in the long run: the second model deployment costs less than the first, and the tenth costs less than the second. FAQ A common pattern is to confuse an MLOps platform purchase with an MLOps practice. The platform is the easy part; the practice is the part that determines whether the second model deployment is cheaper than the first.
