#Introduction

The Logging module in Alauda AI's Monitoring & Ops suite is a real-time logging solution designed for inference services in MLOps/LLMOps/GenOps workflows. It provides instant visibility into the operational status of replica pods powering your AI services, enabling efficient debugging and observability. By streaming container logs with millisecond-level latency and offering built-in analysis tools, it helps users maintain service health while accelerating incident response.

#Advantages

The core advantages of the Logging module are:

• Real-time Streaming
  Automatically captures and displays new log entries from replica pods as they occur, with low latency. Supports live tailing for continuous monitoring of service behavior during model inference.

• Unified Operations Interface
  Embeds directly within the inference service management console, correlating log data with deployment metrics, model versions, and infrastructure status for holistic troubleshooting.

#Application Scenarios

Key use cases for the Logging module include:

• Production Incident Response
  Quickly diagnose model serving errors by searching exception stack traces within individual replica pods, with timestamp alignment to deployment events and traffic spikes.

• Continuous Delivery Validation
  Monitor rolling update processes in real-time, verifying new model deployments by watching for successful health checks and initialization messages across pod replicas.