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This cluster provides a comprehensive view of the concepts, methods, and technologies of Artificial Intelligence and Machine Learning. It covers fundamental principles, use cases, and current trends in the industry.
This segment describes the deployment of machine learning models in production environments. It includes inference mechanisms, interfaces, latency requirements, and scaling aspects. The focus is on using models as part of systems rather than developing them.
Inference is the application of a trained model to new data to produce predictions or decisions. It focuses on latency, scalability and resource optimization for production use.
Concept and practice for reliably delivering, operating and versioning trained machine learning models in production environments.
Concepts and practices for exposing trained machine learning models to production traffic, focusing on scalability, versioning and observability.