Automatic Incident Creation (AIC) events for the Teradata Machine Learning Engine helps administrators detect and manage situations where there is cluster degradation and down incidents.
The Containerized Metadata Persistence (CMDP) feature preserves metadata so that recovery from container or pod failures is possible by restarting the cluster, without the need to reinstall analytic functions on restart.
Teradata Vantage uses containers managed by Kubernetes, providing orchestration framework and containerization.
The In Memory Analytics Tables (IMAT) enhance performance by keeping data in memory and reducing data copies.
Machine Learning Engine Function Execution
The ML Engine function execution feature enables Teradata SQL Engine to run functions that are remotely available on Teradata ML Engine.
Query monitoring helps administrators measure compute resource utilization. Query monitoring is useful in identifying expensive phases of a query that tax certain resources.
Teradata Vantage can monitor the Teradata ML Engine pod resource consumption, correct any resource bottlenecks, and pinpoint long-term and cyclical utilization trends, as well as set health thresholds.
The Workload Management (WLM) manages workload performance by prioritizing workload requests, monitoring system activity, and acting when predefined limits are reached.