Mini toolsDefinition
A set of practices that combines Machine Learning DevOps and Data Engineering to deploy and maintain ML systems in production.
Detailed Explanation
MLOps encompasses the entire lifecycle of ML systems including experiment tracking model versioning deployment automation monitoring and maintenance. It focuses on reproducibility automation and collaboration while ensuring production ML systems remain reliable and scalable. Includes practices for data management model training deployment and monitoring.
Use Cases
Enterprise ML system management Automated model lifecycle handling Production AI system maintenance Cross-team ML collaboration
