#Introduction

#Monitoring & Ops Introduction

Monitoring & Ops is a core module of the Alauda AI platform designed specifically for AI inference service operations. It provides comprehensive observability and operational capabilities across the full lifecycle of inference services, enabling unified management of logs and multi-dimensional metrics through integrated monitoring dashboards. As a critical component of Alauda AI's MLOps/LLMOps/GenOps solutions, it empowers teams to ensure service reliability, optimize resource utilization, and accelerate incident response.

This module focuses on two key operational aspects:

• Logging: Real-time streaming of inference service replica pod logs
• Monitor: Multi-dimensional performance dashboards covering infrastructure, GPU resources, and API traffic

#Advantages

The core advantages of Monitoring & Ops are:

• Real-Time Log Streaming

  • Provides instant access to pod-level logs from inference service replicas
  • Enables rapid debugging and traceability of service requests

• Multi-Dimensional Monitoring

  • Resource Monitor: Tracks CPU/Memory usage for infrastructure health assessment
  • Computing Monitor: Monitors GPU utilization and VRAM allocation for accelerated computing
  • Other Monitor: Measures API-level metrics including Token consumption and Request throughput

• Unified Operations View

  • Aggregates critical operational data across physical resources, GPU clusters, and service endpoints
  • Delivers correlated insights through purpose-built dashboards for AI workloads

• MLOps Ecosystem Integration

  • Seamlessly connects with Alauda AI's model management and deployment pipelines

#Application Scenarios

Monitoring & Ops is essential for:

• Production Model Operations

  • Monitor real-time performance of deployed AI models
  • Track GPU utilization efficiency during high-concurrency inference

• Resource Optimization

  • Identify underutilized resources through historical metrics analysis
  • Right-size deployments based on CPU/Memory/GPU usage patterns

• Performance Benchmarking

  • Compare token processing rates across model versions
  • Analyze request latency distributions under different loads

• Incident Investigation

  • Correlate error logs with resource saturation events
  • Diagnose OOM issues through memory usage timelines