Production LLM Evaluation and Observability

Build and Evaluate Agentic RAG Application with DeepEval, Custom Metrics and Langfuse

What you'll learn

⚡ Build and evaluate an Agentic RAG chatbot using practical LLM evaluation and testing techniques.
⚡ Understand the fundamentals of LLM evaluation, including human vs automated evaluation, offline vs online evaluation, and reference-based vs reference-free eval
⚡ Create and use golden datasets to systematically evaluate the quality and performance of LLM applications.
⚡ Implement LLM evaluation metrics with DeepEval, including built-in and custom evaluation metrics.
⚡ Evaluate RAG and LLM applications for metrics such as correctness, relevance, faithfulness, completeness, and other quality dimensions.
⚡ Build an automated LLM evaluation pipeline to measure application performance consistently.
⚡ Implement LLM observability with Langfuse to trace application workflows, monitor LLM interactions, and analyze performance.
⚡ Use evaluation and observability data to identify problems and improve LLM applications before and after production deployment.

Requirements

❗ Strong Python programming knowledge is required.
❗ Basic knowledge of RAG applications and LangChain is recommended.
❗ Basic understanding of LLMs and Generative AI will be helpful.
❗ An OpenAI API key with available credits is required if you want to use the same OpenAI models used in the course. API usage may incur additional costs.

Description

This course teaches you how to evaluate, test, and monitor real world LLM applications before and after they reach production. You will build an Agentic RAG chatbot and use DeepEval for LLM evaluation and Langfuse for LLM observability, tracing, and monitoring. Build a Practical LLM Evaluation and Observability Workflow.
In this course, you will
1. Learn the fundamentals of LLM evaluation and why traditional testing is not enough for AI applications.
2. Understand human vs automated evaluation and when to use each approach.
3. Learn offline and online evaluation for LLM applications.
4. Create and work with golden datasets for systematic evaluation.
5. Understand reference based and reference free evaluation.
6. Build and use different LLM evaluation metrics.
7. Create custom evaluation metrics for application-specific

Requirements

.
8. Use DeepEval to build an automated LLM evaluation pipeline.
9. Evaluate an Agentic RAG chatbot using practical evaluation techniques.
10. Learn LLM observability with Langfuse.
11. Trace LLM calls, retrieval steps, tool calls, and application workflows.
12. Monitor evaluation scores and use observability data to understand application behavior.
13. Connect evaluation and observability to create a more reliable production workflow.
What You Will Build
Throughout the course, you will work with an Agentic RAG chatbot and gradually add an evaluation and observability layer around it.
You will first understand the application architecture, then build an evaluation workflow using DeepEval, and finally add Langfuse for tracing and observability.
By the end of the course, you will understand how the different pieces fit together
Build → Evaluate → Trace → Monitor → Improve
Whether you are building RAG applications, AI agents, chatbots, or other LLM powered applications, the concepts and techniques in this course can be applied to a wide range of production AI systems.
Who Is This Course For?
This course is designed for AI engineers, software developers, ML engineers, LLM application developers, and anyone building production LLM applications who wants to learn practical LLM evaluation and observability.
You do not need to be an expert in evaluation frameworks. We will start from the fundamentals and progressively build the complete workflow using DeepEval and Langfuse.
By the end of this course, you will have the knowledge and practical skills to evaluate, monitor, debug, and improve the quality of your LLM applications for production.

Who this course is for

⭐ AI and ML engineers who want to learn practical LLM evaluation and observability.
⭐ Generative AI and LLM developers building RAG and AI applications.
⭐ Python developers who want to add evaluation, testing, and monitoring to their LLM applications.
⭐ AI engineers and developers interested in tools such as DeepEval and Langfuse for building reliable LLM applications.
⭐ Anyone with basic RAG and LangChain knowledge who wants to learn LLM evaluation, observability, and evaluation pipelines through hands on projects.

Homepage

https://www.udemy.com/course/production-llm-evaluation-and-observability

Rapidgator
dblqe.Production.LLM.Evaluation.and.Observability.part1.rar.html
dblqe.Production.LLM.Evaluation.and.Observability.part3.rar.html
dblqe.Production.LLM.Evaluation.and.Observability.part2.rar.html
dblqe.Production.LLM.Evaluation.and.Observability.part4.rar.html
dblqe.Production.LLM.Evaluation.and.Observability.part5.rar.html
AlfaFile
dblqe.Production.LLM.Evaluation.and.Observability.part5.rar
dblqe.Production.LLM.Evaluation.and.Observability.part2.rar
dblqe.Production.LLM.Evaluation.and.Observability.part4.rar
dblqe.Production.LLM.Evaluation.and.Observability.part3.rar
dblqe.Production.LLM.Evaluation.and.Observability.part1.rar
DDownload
dblqe.Production.LLM.Evaluation.and.Observability.part4.rar
dblqe.Production.LLM.Evaluation.and.Observability.part5.rar
dblqe.Production.LLM.Evaluation.and.Observability.part2.rar
dblqe.Production.LLM.Evaluation.and.Observability.part3.rar
dblqe.Production.LLM.Evaluation.and.Observability.part1.rar
FreeDL
dblqe.Production.LLM.Evaluation.and.Observability.part5.rar.html
dblqe.Production.LLM.Evaluation.and.Observability.part3.rar.html
dblqe.Production.LLM.Evaluation.and.Observability.part4.rar.html
dblqe.Production.LLM.Evaluation.and.Observability.part1.rar.html
dblqe.Production.LLM.Evaluation.and.Observability.part2.rar.html