Implement AI Observability for Production AI Systems

Welcome to this essential guide on AI Observability. Here, you will learn how to implement comprehensive monitoring for your AI systems, covering critical aspects like logging, tracing, metrics, and cost management. Discover best practices for tracking prompts, responses, latency, and overall performance to ensure your AI models operate reliably in production environments.

Chapters

1. 01 OpenTelemetry Tracing for AI Observability in Python 16m
2. 02 Debugging AI: Pinpointing Issues in Prompts, Models, and Data 16m
3. 03 Hands-On Project: End-to-End AI Observability Implementation 20m
4. 04 Monitor AI Model Performance and System Health with KPIs 19m
5. 05 Structured Logging for AI: Capture Key Interaction Data 15m
6. 06 Real-time Insights: Dashboards, Alerting, and Anomaly Detection 16m
7. 07 Securing Your AI Data: Privacy, Compliance, and Responsible Logging 17m
8. 08 Implement Distributed Tracing in AI Workflows with OpenTelemetry 23m
9. 09 Monitor AI Token Usage & API Costs in Python with OpenTelemetry 17m
10. 10 AI Observability: Why It Matters and How It Works 17m