Prometheus on AI VOID

Key Performance Indicators: Metrics for AI Models and Systems

Fri, 20 Mar 2026 00:00:00 +0000

Introduction: The Pulse of Your AI System

Welcome back, fellow AI adventurer! In previous chapters, we laid the groundwork for AI observability by exploring the crucial roles of structured logging and distributed tracing. We learned how to capture events and flow within our AI applications. But what about understanding the health and performance at a glance? How do we know if our models are performing well, if users are happy, or if costs are spiraling out of control?

Real-time Insights: Dashboards, Alerting, and Anomaly Detection

Fri, 20 Mar 2026 00:00:00 +0000

Introduction: From Data to Actionable Insights

Welcome back, intrepid AI observability enthusiast! In our previous chapters, we embarked on a fascinating journey, learning how to instrument our AI applications with comprehensive logging, tracing, and metrics collection. We discovered how to capture rich data about prompts, responses, model performance, and even the often-elusive costs associated with running our intelligent systems.

But collecting data is only half the battle. Imagine having a treasure chest full of gold, but no map to find it or tools to spend it. That’s what raw observability data can feel like without the right mechanisms to visualize, interpret, and act upon it. This chapter is all about transforming that raw data into powerful, real-time insights that empower you to understand your AI systems at a glance, anticipate problems before they escalate, and react swiftly to unexpected behaviors.

Monitoring and Observability for Production LLMs

Fri, 20 Mar 2026 00:00:00 +0000

Monitoring and Observability for Production LLMs

Welcome back, fellow MLOps engineers and data scientists! In our previous chapters, we’ve explored the exciting world of building robust LLM inference pipelines, optimizing them for GPU usage, implementing smart caching strategies, and designing for scalability. We’ve laid a strong foundation, but there’s a crucial piece missing: How do we know if our systems are actually performing as expected in the wild? How do we catch issues before our users do?

Hands-On Project: End-to-End AI Observability Implementation

Fri, 20 Mar 2026 00:00:00 +0000

Introduction

Welcome to the grand finale of our AI Observability journey! In previous chapters, we’ve explored the theoretical foundations of logging, tracing, and metrics for AI systems, understanding what they are and why they’re crucial. Now, it’s time to roll up our sleeves and bring these concepts to life with a hands-on project.

This chapter will guide you through building a complete, end-to-end observability pipeline for a simple Large Language Model (LLM) application. We’ll instrument our Python-based LLM service using OpenTelemetry for distributed tracing, custom metrics, and structured logging. Then, we’ll deploy an observability backend (SigNoz, which bundles Prometheus and Grafana) using Docker to collect, store, and visualize all our precious AI operational data. Get ready to see your AI system’s inner workings like never before!

Chapter 14: DevOps Best Practices, Monitoring & Troubleshooting

Mon, 12 Jan 2026 00:00:00 +0000

Introduction

Welcome to Chapter 14! You’ve come a long way, building a solid foundation in Linux, version control with Git, mastering CI/CD with GitHub Actions and Jenkins, containerizing applications with Docker, and orchestrating them with Kubernetes. You’ve even set up robust web servers with Nginx and Apache. That’s a huge achievement!

However, the journey doesn’t end when your application is deployed. In the real world, systems can be complex, and things will go wrong. This is where DevOps truly shines: not just in building and deploying, but in maintaining, observing, and continuously improving your systems in production. This chapter will equip you with the knowledge and tools to ensure your applications run reliably, efficiently, and securely.

Chapter 16: Monitoring and Debugging Vector Search Systems

Tue, 17 Feb 2026 00:00:00 +0000

Introduction

Welcome to Chapter 16! So far, we’ve explored the fascinating world of vector search, diving deep into USearch and its powerful integration with ScyllaDB. We’ve learned how to store, index, and query high-dimensional vectors, enabling intelligent applications like recommendation engines and semantic search. But what happens when things don’t go as planned? How do you ensure your vector search system is performing optimally, and what do you do when it’s not?