LangFuse LLM Observability, Tracing, Evaluation, Monitoring
Published 6/2026
Created by Uplatz Training
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 20 Lectures ( 9h 48m ) | Size: 4.5 GB
Learn LangFuse for LLM tracing, observability, evaluation, prompt management, monitoring, and production AI systems.
What you'll learn
Understand the architecture, components, and core concepts of LangFuse. Deploy and configure LangFuse locally using Docker Compose. Set up organizations, projects, users, and API keys within LangFuse. Instrument LLM applications using the LangFuse SDK and OpenAI-compatible APIs. Capture, analyze, and visualize traces, spans, and observations for AI applications. Monitor LLM interactions, token consumption, latency, and operational costs. Implement prompt versioning and prompt lifecycle management for production environments. Enrich traces with metadata, structured context, and custom attributes. Design and implement evaluation frameworks using scores, feedback, and quality metrics. Analyze model performance and identify bottlenecks affecting application reliability. Debug failed, incomplete, or inconsistent traces using LangFuse observability tools. Monitor and evaluate agentic AI workflows, tool-calling agents, and multi-step reasoning systems. Apply observability best practices to LangChain, LangGraph, OpenAI, and custom AI applications. Establish production-grade monitoring, governance, and safety practices for LLM-powered systems. Evaluate self-hosted versus managed LangFuse deployments and select the right architecture. Integrate LangFuse into CI/CD pipelines, testing frameworks, and software development workflows. Identify and avoid common observability anti-patterns encountered in real-world AI projects. Build a fully instrumented, observable LLM application from scratch using industry best practices. Create dashboards and monitoring strategies that support long-term operational maturity. Gain practical skills required for LLMOps, AI Platform Engineering, GenAI Engineering, and AI Operations roles.Requirements
Enthusiasm and determination to make your mark on the world!Description
A warm welcome toLangFuse for LLMOps: Observability, Tracing, Evaluation & Monitoringcourse byUplatz.
LangFuseis an open-source LLM observability and evaluation platform that helps AI engineers trace, monitor, debug, evaluate, and optimize production-grade AI, RAG, and agentic applications.
Large Language Models (LLMs) have transformed the way modern applications are built, but deploying AI systems into production introduces new challenges around visibility, debugging, monitoring, evaluation, cost control, and reliability. Traditional monitoring tools are not designed to understand prompts, model responses, retrieval pipelines, agent workflows, or AI-specific performance metrics.
This course provides a comprehensive, hands-on introduction toLangFuse, one of the leading open-source platforms for LLM observability, tracing, prompt management, evaluation, and production monitoring. You will learn how to instrument AI applications, capture traces, analyze model behavior, monitor token usage and costs, evaluate response quality, and build reliable AI systems that can be confidently deployed at scale.
Starting from the fundamentals, you will explore LangFuse architecture, traces, spans, observations, sessions, and observability concepts before moving into practical implementation using the LangFuse SDK and OpenAI-compatible APIs. Through real-world examples, you will learn how to monitor LLM interactions, manage prompt versions, enrich traces with metadata, collect feedback, perform evaluations, and identify performance bottlenecks.
The course also covers advanced topics including observability for Retrieval-Augmented Generation (RAG) applications, agentic AI systems, production readiness, self-hosting strategies, CI/CD integration, testing workflows, and common implementation mistakes. Finally, you will build a complete mini-project where you instrument a real AI application and apply industry best practices for monitoring, debugging, and optimization.
Whether you are building chatbots, AI copilots, RAG systems, AI agents, or enterprise-grade GenAI applications, this course will equip you with the practical skills needed to monitor, evaluate, and improve AI systems in production.
What You Will Learn
Understand LangFuse architecture and core observability concepts. Deploy and configure LangFuse using Docker Compose. Instrument LLM applications using the LangFuse SDK. Capture and analyze traces, spans, and observations. Monitor token consumption, latency, and operational costs. Implement prompt versioning and prompt management workflows. Add metadata and structured context to AI interactions. Build evaluation pipelines using scores, feedback, and quality metrics. Debug failed, incomplete, and underperforming AI workflows. Monitor RAG pipelines and agentic AI systems. Integrate LangFuse into testing and CI/CD workflows. Apply production-grade monitoring and observability practices.Why Learn LangFuse?
As organizations increasingly deploy AI applications into production, observability and monitoring have become critical Requirements. LangFuse has emerged as a leading platform for understanding how AI systems behave, helping teams improve reliability, reduce costs, accelerate debugging, and continuously enhance application quality. Learning LangFuse provides valuable skills for AI Engineering, GenAI Engineering, LLMOps, MLOps, Platform Engineering, and AI Architecture roles.
By the end of this course, you will have the knowledge and practical experience required to implement enterprise-grade observability and monitoring for modern AI applications.
This course is ideal for professionals building, monitoring, and optimizing production AI applications. LangFuse skills are particularly valuable in roles focused on LLMs, GenAI, observability, evaluation, and AI operations.
AI Engineer - Build and deploy AI-powered applications using LLMs and modern AI frameworks.Generative AI Engineer - Develop chatbots, copilots, RAG systems, and agentic AI solutions.LLM Engineer - Design, optimize, evaluate, and monitor Large Language Model applications.LLMOps Engineer - Manage observability, tracing, evaluation, monitoring, and AI operations in production.Machine Learning Engineer - Deploy and maintain ML and AI systems with production-grade monitoring.AI Platform Engineer - Build and support enterprise AI platforms, tooling, and infrastructure.AI Solutions Architect - Design scalable, reliable, and observable AI solutions for organizations.LangFuse for LLMOps: Observability, Tracing, Evaluation & Monitoring - Course Curriculum
Module 1: Course Foundation and Environment SetupLesson 1: Course Orientation and Environment Validation
Course Overview and Learning Outcomes Understanding the LangFuse Ecosystem Prerequisites and Development Requirements Environment Validation ChecklistLesson 2: LangFuse Architecture Overview (Mental Model First)
What is LangFuse? Core Components and Architecture Data Flow in LLM Observability Mental Models for Tracing and MonitoringLesson 3: Preparing the Local Workspace (WSL2-Only)
Setting up WSL2 Environment Installing Required Dependencies Development Environment Preparation Workspace ValidationLesson 4: Deploying LangFuse with Docker Compose (Local)
LangFuse Deployment Architecture Docker Compose Configuration Running LangFuse Locally Verifying Deployment and ServicesLesson 5: First Login, Organization, and Project Setup
Initial Login and Configuration Organizations and Projects API Keys and Authentication Basic Platform NavigationModule 2: Core Observability and InstrumentationLesson 6: Understanding Traces, Spans, and Observations
Introduction to Traces Understanding Spans Observations and Event Tracking Observability FundamentalsLesson 7: LangFuse SDK Basics (Python Example)
LangFuse Python SDK Introduction SDK Installation and Configuration Creating Basic Traces Logging and Monitoring ExamplesLesson 8: Instrumenting LLM Calls (OpenAI-Style APIs)
Capturing LLM Requests and Responses OpenAI-Compatible Integrations Monitoring Token Usage Tracking Model PerformanceModule 3: Prompt Management and MetadataLesson 9: Prompt Versioning and Prompt Management
Prompt Lifecycle Management Prompt Version Control Managing Prompt Iterations Best Practices for Production PromptsLesson 10: Metadata and Structured Context
Working with Metadata Custom Attributes and Tags Context Enrichment Structured Observability PatternsModule 4: Evaluation, Feedback, and AnalyticsLesson 11: Scores, Feedback, and Evaluation
Evaluation Frameworks Human Feedback Collection Quality Scoring Mechanisms Building Evaluation PipelinesLesson 12: Cost, Latency, and Performance Analysis
Cost Tracking and Optimization Latency Monitoring Throughput and Performance Metrics Production Performance AnalysisLesson 13: Debugging Failed or Partial Traces
Common Trace Failures Root Cause Analysis Debugging Techniques Observability Troubleshooting WorkflowsModule 5: LangFuse for Advanced AI ArchitecturesLesson 14: LangFuse in Agentic Architectures
Observability for AI Agents Multi-Step Agent Tracing Tool Calling Visibility Agent Workflow MonitoringLesson 15: Production Readiness and Safety
Production Monitoring Strategies Reliability and Governance Safety and Risk Monitoring Operational Best PracticesLesson 16: Self-Hosting Considerations
Cloud vs Self-Hosted Deployment Infrastructure Requirements Scalability Considerations Security and ComplianceModule 6: Enterprise Integration and OperationsLesson 17: LangFuse with CI/CD and Testing
Observability in Development Pipelines Automated Testing Workflows CI/CD Integration Patterns Quality Gates and MonitoringLesson 18: Common Anti-Patterns and Mistakes
Frequent Implementation Errors Observability Pitfalls Monitoring Blind Spots Lessons Learned from Production SystemsModule 7: Hands-On Industry ProjectLesson 19: Mini Project - Instrumenting a Real LLM Application
Designing an Observable LLM Application End-to-End Instrumentation Monitoring User Interactions Performance and Evaluation AnalysisModule 8: Course Wrap-Up and Next StepsLesson 20: Course Conclusion
Operational Maturity Model for LLM Applications Long-Term Observability Strategy Scaling Monitoring Practices Professional Deployment Discipline Next Learning Pathways and ResourcesWho this course is for
AI Engineers who want to monitor, evaluate, and optimize LLM-powered applications in production. GenAI Engineers building applications using OpenAI, Anthropic, Gemini, LangChain, LangGraph, CrewAI, AutoGen, or similar frameworks. LLMOps Engineers responsible for observability, reliability, governance, and operational excellence of AI systems. MLOps Engineers looking to extend traditional ML monitoring practices to Large Language Model applications. Machine Learning Engineers who want to understand tracing, evaluation, prompt management, and production monitoring. Data Scientists deploying AI solutions and seeking visibility into model behavior, performance, and user interactions. Software Engineers integrating LLMs into web, mobile, SaaS, or enterprise applications. Platform Engineers and DevOps Engineers supporting AI infrastructure and operational workflows. Cloud Engineers managing AI deployments on AWS, Azure, GCP, or hybrid environments. AI Solution Architects designing scalable, observable, and enterprise-grade AI systems. Technical Leads and Engineering Managers overseeing AI application development and production operations. Product Managers working on AI products who want to understand monitoring, evaluation, and user feedback loops. Startup Founders and AI Consultants building customer-facing AI applications and services. Students and professionals looking to develop practical skills in LLM observability and modern AI operations.Homepage
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