MLops

MLOps is a set of practices that aims to unify machine learning system development (Dev) and ML system operation (Ops). It extends DevOps principles to the lifecycle of machine learning models, including data management, model training, deployment, monitoring, governance, and retirement. The goal is to improve speed, reliability, reproducibility, and governance of ML systems in production.

A typical ML lifecycle includes data management and preparation, model development, validation, deployment, monitoring, and retraining. MLOps emphasizes automation, versioning, and collaboration across data engineers, data scientists, software engineers, and IT operations.

Key practices include data versioning and feature stores, model versioning and registries, experiment tracking, automated testing for data quality and model performance, continuous integration/continuous deployment pipelines tailored for ML, canary and blue-green deployment strategies, monitoring for data drift and model performance, automated retraining triggers, and governance and compliance, along with security and access control.

Common tools and platforms support these practices, including MLflow, Kubeflow, DVC, Feast, TensorFlow Extended, and cloud services such as AWS SageMaker, Azure ML, and Google Vertex AI. Deployment typically relies on Kubernetes or other orchestration frameworks to enable scalable, reliable inference, automated rollbacks, and reproducibility across environments.

Challenges facing MLOps include data drift, model bias, reproducibility, privacy and security concerns, regulatory compliance, and the need for cross-functional skills. As a discipline, MLOps sits at the intersection of software engineering, data engineering, and IT operations, and is often described as an extension of DevOps tailored to the machine learning lifecycle.