InfluxDB for Model Monitoring: Metrics, Observability, and Performance Tracking for Modern Applications

By Alex Ming

InfluxDB for Model Monitoring teaches how to observe and measure the behavior of language-driven applications using time-series data. The book focuses on building dashboards and alerting systems that reveal what is truly happening inside production workloads.
Readers discover how to collect inference latency, token throughput, error rates, GPU utilization, and custom business metrics into InfluxDB. Step-by-step tutorials show how to design schemas, write efficient queries, and create real-time visualizations with Grafana and native tools.
Inside the book you will learn: Designing time-series structures for model workloadsWriting data from Python and application serversCreating dashboards for latency and usage trendsDetecting anomalies and driftSetting up alerts and notificationsRetention policies and storage optimizationIntegrating logs with metrics for deeper insightThe focus is practical observability knowing when systems are slowing down, why costs are rising, and how users are interacting with deployed models. Examples are tool-agnostic and can be applied to cloud or on-premise environments.
This book is ideal for engineers who need reliable monitoring rather than guesswork.

• Format: Paperback
• Language: English
• ISBN: B0GMQFS2LG
• ISBN13: 9798247662914
• Release Date: February 2026
• Publisher: Independently Published
• Length: 188 Pages
• Weight: 0.74 lbs.
• Dimensions: 0.4" x 7.0" x 10.0"