Title: MLOps Author: Kriko Published: May 30, 2023 Last modified: Jul 7, 2026 --- 1. [Home](https://kriko.io/) / 2. [Glossary](https://kriko.io/glossary) / 3. M Letter # MLOps **MLOps** is a collection of practices and processes used to develop, deploy, monitor and manage machine learning models in a sustainable manner. The term combines Machine Learning with DevOps. It is commonly described as machine learning operations. MLOps aims to strengthen collaboration between [data](https://kriko.io/services/data-analysis-insight) scientists, machine learning engineers, software developers and IT operations teams. It enables models created in experimental environments to be transferred reliably into production systems. Activities such as model development, [data](https://kriko.io/services/data-analysis-insight) preparation, training, testing, validation and deployment are managed within a shared workflow. This creates a more organised and traceable structure instead of allowing different teams to work in isolation. Scalability, reproducibility, automation and collaboration are among the main principles of MLOps. A model should be reproducible using the same [data](https://kriko.io/services/data-analysis-insight), configurations and parameters to ensure reliable results. Model versions, datasets, source code and training settings should therefore be recorded and managed systematically. This approach makes it easier to trace changes and return to a previous version when necessary. MLOps is not limited to training and deploying a machine learning model. It also includes monitoring model performance in production, identifying changes in [data](https://kriko.io/services/data-analysis-insight) distribution and retraining models when required. User behaviour, market conditions and [data](https://kriko.io/services/data-analysis-insight) sources may change over time, causing model accuracy to decline. **Model monitoring** and regular performance assessment are therefore essential components of the MLOps lifecycle. Modern MLOps systems may use continuous integration and continuous deployment practices. Changes to code, [data](https://kriko.io/services/data-analysis-insight) pipelines or model architecture can be evaluated through automated tests. Models that meet the required performance and approval criteria can then be deployed into production. This structure reduces manual work and helps prevent deployment errors and inconsistencies. MLOps can be used in industries such as retail, finance, healthcare, aviation, telecommunications and e-commerce. Retail companies may use machine learning models to forecast product demand and manage inventory levels. Airlines can provide personalised offers based on user behaviour or develop dynamic pricing models. Financial institutions may apply MLOps to fraud detection, risk analysis and customer churn prediction. An MLOps lifecycle generally includes [data](https://kriko.io/services/data-analysis-insight) collection, [data](https://kriko.io/services/data-analysis-insight) preparation, model development, validation, deployment and monitoring. After a model enters production, performance results are fed back into the development process. This feedback loop helps models adapt to changing business conditions and newly available information. Machine learning projects therefore become continuously improving systems rather than one-off experiments. MLOps can help organisations manage machine learning processes more quickly and consistently. Automation reduces repetitive tasks, shortens development cycles and limits operational errors. Responsibilities between teams become clearer, while the [data](https://kriko.io/services/data-analysis-insight) and conditions used to develop each model remain traceable. This creates a more transparent working environment for both technical teams and business leaders. MLOps practices may also reduce unnecessary resource consumption and the costs caused by unsuccessful deployments. However, these benefits require appropriate infrastructure, clearly defined responsibilities, security policies and governance standards. Access controls, [data](https://kriko.io/services/data-analysis-insight) privacy and model explainability are particularly important for organisations that work with sensitive information. A properly designed **MLOps infrastructure** supports the reliable, scalable and sustainable operation of machine learning systems. In summary, MLOps is a comprehensive approach that moves machine learning projects from experimentation into production and manages their complete lifecycle. It brings data science, software development and operations teams together within a shared framework. It supports process automation, continuous model monitoring and updates based on changing conditions. 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