MLOps

Technical terms
About 1 min read

A framework for automating and managing the lifecycle of machine learning models. It is an extension of DevOps that combines software engineering's CI/CD with 'Continuous Training (CT)' to maintain model performance and stability in production environments.

Also known as
Machine Learning OperationsMLOps

Detailed explanation

MLOps is a methodology and culture that integrates the development (Dev) and operation (Ops) of machine learning (ML) models to ensure experimental reproducibility, rapid deployment, and operational reliability. It automates the entire process—from data collection, preprocessing, model training, and validation to deployment and monitoring—into pipelines. A key core element is Continuous Training (CT), which detects 'model drift' (the decline of model performance over time) and automatically triggers retraining. Recently, it has expanded to LLMOps specialized for managing large language models, helping companies directly translate their AI assets into business value.

Why It Matters in Tool Selection

Since machine learning models rely on constantly changing 'data' rather than static code, their performance inevitably degrades over time. MLOps tools are essential because they prevent this 'technical debt' and provide the infrastructure to stably serve models to tens of thousands of users in actual production environments, rather than just having them work well in laboratory environments.

What to Check

  • The automation level of the Continuous Training (CT) pipeline (manual vs. automated retraining)
  • Whether experimental reproducibility is guaranteed through data and model version control (lineage)
  • Monitoring capabilities to detect model drift and data bias in real time
  • Compatibility with existing cloud infrastructure (AWS, GCP, Azure) and Kubernetes

Examples

When MLOps is introduced in a financial Fraud Detection System (FDS), the system detects the moment the accuracy of the existing model drops due to the emergence of new fraud patterns. It then automatically collects the latest data, retrains the model, and once the validation is passed, immediately deploys it to the production environment to prevent fraud.

Confusing Terms

DevOps

Focuses on code version control and service stability. (Code-centric)

LLMOps

A subconcept of MLOps, specialized in prompt engineering, vector databases, and LLM fine-tuning.

Related terms

DevOpsCI/CD