This lab is currently in Beta, content may be updated as we refine the material
LABINTERMEDIATE

Track Prompt Experiments

Track and compare different prompt strategies using MLflow experiment tracking.

60 minutes
ai-infrastructure/llmops
Track Prompt Experiments - Platform Engineering Hands-On Lab Icon

Lab Overview

🛠 Lab from the AI Platform Engineering Bootcamp. Used in Week 5. Bootcamp landing page: https://academy.tekanaid.com/bootcamps/ai-platform-engineering-bootcamp Parent course(s):

  • Week 5: ML Infrastructure and Experiment Tracking (slug: ml-infrastructure-105)

🟡 Beta bootcamp lab. Hands-on instructions, check scripts, and solve scripts are in place. Lab is part of the running Platform Assistant capstone arc that grows across all 8 weeks of the bootcamp.

Learn to track prompt experiments using MLflow. Log prompt versions, evaluation metrics, and artifacts. Compare different prompt strategies and build a comparison dashboard.

What You'll Learn

Configure MLflow client for prompt experiment tracking

Log prompt variations with parameters, metrics, and artifacts

Compare prompt experiment results using MLflow

Build a custom comparison dashboard

Prerequisites

Understanding of MLflow concepts

Python programming fundamentals

Basic understanding of LLM prompting

Technologies Covered

mlflowpromptsexperiment-trackingllmopsevaluationollama

Part of a Course

This lab is part of the LLMOps and Model Management course

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