What is MLOps (Machine Learning Operations)?
A set of practices that combines machine learning, DevOps, and data engineering to deploy, monitor, and maintain ML models in production reliably and efficiently.
How It Works
MLOps bridges the gap between building ML models and running them in production. It covers the entire ML lifecycle: data preparation, model training, testing, deployment, monitoring, and retraining. Without MLOps, models that work in notebooks fail in production — data drift causes accuracy to degrade, models aren't updated, and there's no visibility into performance. MLOps brings software engineering rigor to machine learning.
Key Benefits
- Reliable model deployment and serving
- Automated retraining when performance degrades
- Version control for models and data
- Monitoring for data drift and model decay
- Faster iteration from experiment to production
Common Use Cases
- Deploying fraud detection models in financial services
- Maintaining recommendation systems for e-commerce
- Running computer vision models for quality control
- Operationalizing NLP models for document processing
Related Terms
AI Agents
Autonomous software programs powered by artificial intelligence that can perceive their environment, make decisions, and take actions to accomplish specific goals.
Data Pipeline
An automated series of steps that move and transform data from one or more sources to a destination system for storage, analysis, or further processing.
ETL (Extract, Transform, Load)
A data integration pattern that extracts data from source systems, transforms it into a suitable format, and loads it into a target system such as a data warehouse.
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