MLOps
Metadata
Fecha: 2026-05-20 Área: Machine Learning
MLOps
MLOps refers to the practice of operationalizing and maintaining machine learning models in production environments reliably and efficiently.
It is a set of practices that combines ML, DevOps, and data engineering to streamline the entire ML lifecycle, from data ingestion and model training to deployment and monitoring.
Key principles of MLOps
- Automation: Automate the entire ML lifecycle
- Version control: Version control for code, data, and models
- Reproducibility: Reproducible ML experiments
- Monitoring: Real-time monitoring of model performance
- CI/CD: Continuous integration and continuous delivery
- Model governance: Ensuring that models are developed, deployed, and used in a responsible and ethical manner
Implementing MLOps
Data Preparation ⇒ Model build ⇒ Model evaluation ⇒ Model selection ⇒ Model deployment ⇒ Model monitoring ⇒ Model retraining
🚧 Plan de Mejora / Tareas Pendientes
Define las tareas que te ayudarán a subir tu nivel-comprension en la próxima revisión. Usa los tags: #mejora-concepto, #mejora-practica, #mejora-analogia.
- Tarea para aclarar una duda de concepto. Usa mejora-concepto
- Tarea para implementar un ejercicio práctico. Usa mejora-practica
- Tarea para crear una analogía o diagrama. Usa mejora-analogia
- Crear Flashcards
🧪 Aplicación
- Explicarlo sin consultar la nota
- Resolver un caso nuevo o escribir un ejemplo
- Compararlo con una alternativa
- Usarlo en un proyecto