MLOps overview

MLOps operationalizes the machine learning lifecycle — taking models from experimental notebooks to production systems and keeping them reliable over time. Key concerns include reproducible training pipelines, model versioning and registries (MLflow, SageMaker), feature stores, CI/CD for ML, A/B testing of model variants, drift detection, and inference serving at scale. MLOps is the production face of Data Science and bridges into DevOps (pipeline automation) and Cloud Infrastructure (GPU compute, managed endpoints). Specializations include LLM fine-tuning and ML inference serving.