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CI/CD Pipeline ML
Automated set of continuous integration and continuous deployment processes specifically adapted to the lifecycle of machine learning models, including data validation, training, testing, and deployment.
ML Model Registry
Centralized repository for storing, versioning, and managing machine learning models throughout their lifecycle.
Automated Model Validation
Process of automatically verifying that machine learning models meet predefined performance, quality, and business requirements before deployment.
ML Monitoring
Continuous tracking of machine learning model performance, data quality, and system health in production to detect issues and ensure reliable operation.
Inference Server
Computational infrastructure designed to serve machine learning model predictions in real-time or batch mode, typically exposing REST or gRPC endpoints for model inference.
ML Ops Pipeline
End-to-end automated workflow that orchestrates the entire machine learning lifecycle from data preparation and model training to deployment and monitoring, often incorporating governance and compliance practices.