Monitoring on English AI Terms Dictionary

AI observability

Sat, 18 Jul 2026 09:44:10 +0000

Definition

AI observability extends traditional software monitoring to address the unique challenges of machine learning systems. It involves tracking model performance, data drift, and inference latency in real-time. Key components include monitoring input data quality, model prediction accuracy, and system resource utilization. By providing deep visibility into the black box of ML models, observability helps engineers detect anomalies, debug issues, and ensure that deployed models continue to perform reliably as underlying data distributions change over time.

Graphs

Sat, 18 Jul 2026 09:32:53 +0000

Definition

While singular ‘graph’ refers to the abstract data structure, ‘graphs’ often denotes either multiple distinct graph instances or visual plots used in ML monitoring. In visualization, line graphs or bar charts display metrics like loss curves or accuracy over epochs. In data modeling, it refers to collections of interconnected entities. Understanding the distinction helps in differentiating between the structural representation of relational data and the analytical visualization of model performance metrics during training and evaluation phases.