Last updated: August 2026
MLOps
MLOps (machine learning operations) is a set of practices that applies DevOps principles to the machine learning lifecycle — data preparation, model training, deployment, monitoring, and governance — with automation and reproducibility at each stage, aimed at reliably running ML models in production, not just producing them in a research notebook.
Why ML needs its own operations discipline
Standard DevOps practices assume code behavior is deterministic — the same code produces the same output every time until the code itself changes. Machine learning breaks this assumption: model behavior depends on training data as much as code, model performance can degrade over time as real-world data drifts from training data, and reproducing a specific model result requires tracking not just code version but also dataset version and training configuration. MLOps extends DevOps practices to handle these ML-specific realities — versioning data and models alongside code, monitoring for performance drift after deployment, and automating retraining pipelines rather than treating model deployment as a one-time event.
What MLOps covers
MLOps spans the full model lifecycle: data preparation and versioning, model training and experiment tracking, model validation against defined performance thresholds, deployment to production serving infrastructure, ongoing monitoring for accuracy and drift, and automated or triggered retraining when performance degrades below an acceptable threshold. It draws on three contributing disciplines — machine learning, software engineering (particularly DevOps), and data engineering — since managing this lifecycle well requires competence in all three, not just ML modeling skill alone.
When it applies
MLOps practices earn their overhead once an organization has more than a handful of models in production, or when a single model's failure or drift has real business consequences that make informal, manual monitoring insufficient. It's particularly important for models that need periodic retraining as underlying data patterns shift — a fraud-detection model, for instance, needs monitoring and retraining discipline because fraud patterns actively evolve to evade a static model, unlike a one-off classification task with stable underlying patterns.
When it doesn't apply
Full MLOps tooling and process is overkill for a single experimental model still being validated for business value, or for a one-off analysis that doesn't need to run repeatedly in production. Building comprehensive MLOps infrastructure before confirming a model actually delivers business value is a common over-investment mistake — validate the model's value with lighter-weight tooling first, then invest in production MLOps infrastructure once that value is confirmed and the model needs to run reliably at scale.
What Code Ninety does
Code Ninety builds MLOps infrastructure for clients once a model has validated business value, covering automated retraining triggers, drift monitoring, and version-tracked deployment — scoped to the client's actual production reliability needs rather than over-building process infrastructure before it's warranted. Code Ninety delivers enterprise AI solutions for regulated industries. See the computer vision case study for a deployed production model.