What is MLOps? - Explanation & Meaning

Definition

MLOps (Machine Learning Operations) is a set of practices and tools that manages the lifecycle of machine learning models — from development and training to deployment, monitoring, and maintenance in production environments.

Technical explanation

MLOps brings DevOps principles to the world of machine learning and adds ML-specific challenges such as data versioning, experiment tracking, and model drift detection. A typical MLOps pipeline includes: data ingestion and validation, feature engineering and feature stores, model training with hyperparameter optimization, model evaluation and validation, model registry for version control, automated deployment (canary, blue-green, or shadow deployments), and production monitoring. Tools in the MLOps ecosystem include MLflow for experiment tracking, Kubeflow for orchestration on Kubernetes, Weights & Biases for visualization, and Seldon/BentoML for model serving. In 2026, LLMOps — a specialization focused on managing LLM-based applications — has emerged with its own challenges: prompt management, RAG pipeline monitoring, token cost tracking, and evaluation of generated output via frameworks such as RAGAS and DeepEval. Feature stores like Feast and Tecton standardize how features are computed and shared between training and inference.

How MG Software applies this

At MG Software, we implement MLOps practices for every AI solution we bring to production. We use automated pipelines for training and deployment, monitor model performance in real-time, and detect data drift before it impacts end users. For LLM applications, we monitor answer quality, latency, and costs.

Practical examples

• A fintech company that set up an MLOps pipeline to automatically retrain their fraud detection model when precision drops below a threshold, ensuring the model stays current with new fraud patterns.
• An e-commerce platform using MLOps to automatically update their recommendation model weekly with new user data, evaluate via A/B tests, and roll out via canary deployments.
• An AI startup using MLflow and Weights & Biases to track hundreds of experiments, select the best model configuration, and automatically deploy to production via a CI/CD pipeline.

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