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 in a Git repository, and retraining requires re-running an expensive compute process rather than recompiling. Understanding what MLOps addresses, and what it doesn’t, is prerequisite to deciding whether and how to invest in it. Model deployment without MLOps Without MLOps practices, ML teams face reproducible 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 6 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 the model on new data but cannot reproduce the original training run to verify the new version is actually better What does MLOps provide? Problem MLOps solution Irreproducible training Experiment tracking (MLflow, W&B), 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 Containerized training and serving environments When 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 Multiple people are working on the same models MLOps is overhead when: You are in a proof-of-concept phase with no production models Models are trained once and essentially static (batch scoring that rarely changes) The team is one person working alone The business impact of model failure is low In our experience, organizations adopt MLOps tooling too early (before they have production models 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. For more on how MLOps applies specifically to organizations 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 supports 5–10 models with modest operational effort. 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 are deployed 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 (2–4 engineers). 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 is strategic (setting policies, reviewing aggregate metrics) rather than operational (triggering pipelines, approving deployments). 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 that the team is not ready to use effectively, resulting in expensive infrastructure that does not deliver its intended value. The business case for MLOps becomes clear when you calculate the cost of operating models without it. Manual model deployment requires 2–4 hours of engineer time per deployment. If models require weekly retraining (common for models operating on changing data), that is 100–200 hours of engineering time per year per model on deployment alone — not counting monitoring, debugging, and retraining effort. At 10 models, manual operations consume 1–2 full-time engineers. MLOps automation reduces per-deployment effort to near zero (automated pipeline) plus monitoring review time (30 minutes per week), freeing those engineers for higher-value work on new model development and system improvement.
