Introduction to MLOps

What Is MLOps?

Machine Learning Operations, or MLOps, combines machine learning development with software engineering and operational practices.

Its purpose is to make the process of developing, deploying, monitoring, and maintaining machine learning systems more reliable and repeatable.

Traditional software generally follows explicitly programmed logic. Machine learning systems also depend on training data, model parameters, evaluation results, and the conditions under which predictions are made.

Key Components of MLOps

Experiment tracking: Records model configurations, training results, and evaluation metrics so experiments can be compared.

Data and model versioning: Tracks which datasets, code, and model artifacts were used to create a particular model.

Automation: Runs repeatable steps such as testing, training, validation, and deployment.

Continuous integration and delivery (CI/CD): Automates software checks and prepares approved changes for release.

Model monitoring: Tracks production behavior, errors, latency, data quality, and model performance where ground-truth labels are available.

Model registry: Stores and organizes model versions, including their metadata and deployment status.

Why Is MLOps Necessary?

Imagine a retail company using an AI model to forecast demand. The model may initially perform well, but customer behavior, seasonal patterns, and product availability can change.

MLOps helps the team track model versions, detect performance problems, reproduce experiments, and safely release updated models.

Without these practices, teams may struggle to understand which model is running, how it was trained, or why its predictions have changed.