Automatic Incident Creation (AIC) events for ML 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.
Failure Detection Recovery (FDR) detects failures and attempts to recover from those failures automatically, thereby reducing cluster downtime.
The In Memory Analytics Tables (IMAT) enhance performance by keeping data in memory and reducing data copies.
Kubernetes is a container orchestration platform. The applications that comprise ML Engine run in containers. Kubernetes launches and operates the container infrastructure that makes up the ML Engine.
Machine Learning Engine Function Execution
ML Engine function execution feature enables Advanced SQL Engine to run functions that are remotely available on 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 ML Engine worker node (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.