Mastering LLM Observability with Arize Phoenix

Level

Intermediate
Duration

4 hours
Last Updated

May 6, 2025
Certificate

Certificate of completion

Understand the critical need for observability in large language model (LLM) applications and the challenges associated with unmonitored LLM behavior.
Gain hands-on experience using Arize Phoenix for real-time monitoring, traceability, and evaluation of LLM outputs.
Learn to identify and measure key performance indicators (KPIs) like coherence, hallucination rate, and drift in LLM-based systems.
Master the ingestion and structuring of LLM trace data (spans, traces, and events) to facilitate transparent and meaningful analysis.
Explore advanced LLMOps workflows and observability techniques using Arize Phoenix’s architecture, integrations, and MCP Server.

Course Curriculum

Overview of LLM Observability and Its Need

Deep Dive into LLM Observability

32:12
LLM Observability and its Importance
Identifying KPIs relevant to LLMs

48:40
Key Performance Indicators for Measuring LLM Performance

Understanding Arize AI and Phoenix Platform

Data Ingestion and Logging for LLMs in Phoenix

Core LLM Observability Features of Arize Phoenix

Self-Assessment

Student Ratings & Reviews

1.0

Total 1 Rating

0 Rating

1 Rating

Mohan Khilariwal

1 month ago

Modules seems disjoint and looks more focused on the individual tools demo rather actually system design and going step by step.. Moreover the reading content is not downloadable in most of the cases in all the programs..