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.
Key practices include data versioning and feature stores, model versioning and registries, experiment tracking, automated testing
Common tools and platforms support these practices, including MLflow, Kubeflow, DVC, Feast, TensorFlow Extended, and cloud
Challenges facing MLOps include data drift, model bias, reproducibility, privacy and security concerns, regulatory compliance, and