AI concepts
MLOps (DevOps for machine learning)
A set of practices that automate the lifecycle of a machine learning model from research experiment to production and maintenance. It covers versioning of models and data, automated training pipelines, deployment, monitoring and retraining. An organisation's MLOps maturity is the single strongest predictor of sustained AI ROI in the second and third year after deployment.
Primary source: Google AI Hidden Technical Debt 2015, Microsoft AI Maturity Model 2024, AWS Machine Learning Lens 2025
The term comes from the foundational Google paper "Hidden Technical Debt in Machine Learning Systems" by D. Sculley and others, published in 2015. The paper pointed out that the model code is typically 5 percent of a working production system, with the rest being infrastructure, data, monitoring and integration. Most ML teams in 2015 ignored the other 95 percent. MLOps emerged as the answer.
Three levels of maturity
The Google MLOps Maturity Model standardises the scale:
Level 0: manual. A data scientist runs every training and deployment step by hand. A notebook on a laptop, deployment by "send the file to the admin". Used in PoCs and first deployments. The risk: a production model that cannot be reproduced after six months.
Level 1: ML pipeline automation. Training is automated through a pipeline (Vertex AI, Kubeflow, SageMaker Pipelines), but deployment is still manual. Most companies are at this level in 2025.
Level 2: CI/CD pipeline automation. Full automation from a code commit through to production deployment, with integration tests, performance regression checks and automatic rollback. Used in companies with a mature engineering culture.
Key components
The Microsoft AI Maturity Model of 2024 lists seven: experiment tracking (MLflow, Weights & Biases), a feature store (Tecton, Feast), a model registry, an automated training pipeline, model serving (Kubernetes, SageMaker, Vertex), monitoring and alerting, and CI/CD integration.
The cost of MLOps
The AWS Machine Learning Lens of 2025 estimates that a mature MLOps platform costs 200 to 500 thousand zloty in the first year (mainly engineering time), plus 30 to 50 percent of that cost in subsequent years. That looks expensive until you compare it with the alternative: no MLOps means catastrophic operational risk (models that fail and nobody knows why) and a high staffing overhead (every model update requires manual coordination).
The Polish context
In Polish companies in 2025 to 2026, MLOps maturity is low. Our own estimate: 70 percent of enterprise companies are at level 0 (manual), 25 percent at level 1, and 5 percent at level 2. Most AI projects in Polish companies have not yet hit the scale problem at which MLOps becomes mandatory.
An MLOps maturity assessment is an optional part of the AI Readiness Audit for companies with 3 or more models in production.