Production LLM Monitoring & Optimization

Packt via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

9 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

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Overview

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Uncontrolled LLM usage can silently drain budgets and slow down production systems. This course equips you with practical tracing, instrumentation, and cost analysis techniques to gain full visibility into your AI stack. Learn to detect bottlenecks, control token spending, and operate reliable, cost-efficient LLM applications at scale. 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. 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. The course follows a structured path from business fundamentals to deep technical implementation. It begins with cost visibility concepts, progresses through hands-on platform setup and tracing instrumentation, and culminates in optimization, alerting, and security patterns. Each module reinforces practical deployment with real production scenarios and executable Python examples. This course is based on Production LLM Monitoring: Observability, Tracing & Cost Optimization, by Paulo Dichone. This course is licensed and distributed by Packt. All rights reserved. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Syllabus

Introduction
- This module introduces key concepts in observability and cost optimization, providing learners with an understanding of how these principles impact production LLM operations. It outlines course structure, objectives, and the significance of efficient system management. Learners will gain foundational knowledge to support effective LLM deployment and maintenance.
The Business Case Why Observability = Money
- This module explores the financial and operational benefits of observability in LLM systems, covering cost drivers, architectural differences, and practical tools for demonstrating ROI. Learners will gain insights into how observability supports efficiency, risk reduction, and informed decision-making.
Understanding LLM Costs – Where Your Money Goes
- This module explores how language model costs are calculated, including pricing structures, token usage, and hidden cost factors. Learners will gain insights into managing expenses in RAG and agent-based systems, and how to make cost-effective decisions when deploying language models.
Observability Platform Selection – Langfuse and Hands-on
- This module guides learners through the process of selecting and implementing an observability platform for LLM systems, with a focus on Langfuse. It covers setup, data modeling, API integration, and practical trace analysis to help learners monitor and optimize LLM applications effectively.
Instrumenting Your LLM Application
- This module teaches how to instrument and monitor large language model (LLM) applications, focusing on capturing telemetry, tracing multi-step pipelines, and integrating observability tools. Learners will gain practical skills in tracking performance, cost, and interactions within LLM systems.
Cost Optimization Strategies That Work
- This module explores practical techniques for reducing costs in LLM workflows, including prompt optimization, semantic caching, and smart model routing. Learners will gain insights into how to implement cost-effective strategies while maintaining high performance. The content emphasizes real-world applications and measurable financial benefits.
Monitoring, Alerting & Debugging
- This module equips learners with the skills to set up and manage effective monitoring and alerting systems. It covers configuring dashboards, implementing automated notifications, and debugging through trace analysis. Learners will gain practical knowledge to maintain efficient large language model operations.
Production Patterns & Security
- This module focuses on implementing security controls, compliance practices, and real-world production patterns to enhance the reliability, scalability, and security of applications. Learners will gain practical knowledge on protecting sensitive data and optimizing deployment processes.
Wrap up and Next Steps
- This module helps learners reinforce key concepts from the course and provides practical guidance on implementing observability and cost optimization in real-world LLM environments. It offers a structured approach to applying learned strategies and planning future steps.