Production LLM Monitoring: Observability, Tracing & Cost Optimization.

Format:

Video

Language:

English

Physical Description:

1 online resource (156 minutes)

Place of Publication:

[Place of publication not identified] : PACKT Publishing, 2026.

Language Note:

In English.

System Details:

video file

Summary:

<b>Build visibility into every LLM call with tracing, monitoring, and cost governance techniques. Gain practical skills to prevent token overruns, optimize pipelines, and run reliable AI systems in production.</b><h4>Key Features</h4><ul><li>Comprehensive LLM observability framework linking tracing data to measurable ROI impact</li><li>Vendor neutral platform comparison enabling informed tooling decisions for AI teams</li><li>Hands on production instrumentation patterns built around real RAG system workflows</li></ul><h4>Book Description</h4>Production LLM systems introduce unpredictable token usage, hidden RAG multipliers, and debugging blind spots that traditional monitoring cannot solve. This course begins by building the business case for observability, demonstrating how tracing and cost transparency directly impact ROI. You will explore how LLM costs accumulate, where money leaks inside pipelines, and why traditional observability models fall short for generative AI workloads. The journey then moves into platform evaluation and hands-on implementation. You will set up Langfuse, understand its data model, create traces, and instrument multi-step RAG workflows. Framework integrations such as LangChain are covered to show how real production systems capture spans, metadata, and token usage. Each step transforms abstract monitoring theory into practical, deployable code patterns. In the final sections, the focus shifts to optimization and operational excellence. You will implement prompt tuning, semantic caching, smart model routing, cost alerts, and monitoring dashboards. The course concludes with security patterns, PII redaction strategies, and enterprise-ready production practices, ensuring you leave with a complete observability and cost governance framework.<h4>What you will learn</h4><ul><li>Implement Langfuse tracing for production LLM apps</li><li>Analyze token usage and hidden pipeline costs</li><li>Instrument multi-step RAG workflows effectively</li><li>Optimize prompts to lower token consumption</li><li>Deploy semantic caching for cost reduction</li><li>Configure cost alerts and monitoring dashboards</li></ul><h4>Who this book is for</h4>This course is designed for ML Engineers, AI Engineers, backend developers, and technical leads responsible for deploying and maintaining LLM-powered systems in production. It is particularly valuable for professionals managing API budgets, RAG pipelines, or multi-step agent workflows. Python developers familiar with OpenAI, Anthropic, or similar APIs will benefit from learning how to introduce structured tracing, cost controls, monitoring dashboards, and secure production patterns into their existing AI applications.

Notes:

Title from resource description page (viewed July 20, 2026).