Azure AI-102 [5]: AI Model Evaluation | Manual vs Automated Testing | Performance Metrics

Master AI quality assurance! Learn professional evaluation techniques to measure and prove your AI's accuracy, relevance, and performance using both manual review and automated AI-judging systems for AI-102 certification.
What You'll Learn:

Design comprehensive test datasets for AI evaluation
Implement manual evaluation with domain expert review
Configure automated evaluation using GPT-4 as AI judge
Measure relevance, semantic similarity, coherence, and fluency metrics
Compare multiple models and configurations with data-driven decisions
Scale evaluation from dozens to thousands of test cases

Hands-On Lab Includes:
✅ Creating diverse test datasets with expected answers
✅ Performing manual thumbs-up/thumbs-down evaluation
✅ Setting up automated evaluation with Azure AI Foundry
✅ Configuring GPT-4o as judge model for scoring
✅ Running evaluations on semantic similarity and relevance
✅ Generating evaluation reports and metric aggregations
✅ Comparing base model vs fine-tuned model performance
✅ Cost-benefit analysis using evaluation data
Certification Relevance:
Key AI-102 exam topic covering model evaluation methodologies, performance metrics, testing strategies, and quality assurance in Azure AI solutions. Demonstrates professional-grade AI development practices.
Key Concepts Covered:

Manual vs automated evaluation trade-offs
Test dataset design and representativeness
AI judge pattern using GPT-4o
Relevance and semantic similarity metrics
Coherence and fluency assessment
Harmful content detection in responses
A/B testing and model comparison
Data-driven model selection
Evaluation report interpretation

Official Resources:
📚 Microsoft Learn Course: https://learn.microsoft.com/en-us/cre...
💻 GitHub Lab Files: https://github.com/asiandevs/AI-102_A...
NOTE: Follow the official LAB link from the link file of my repository.
Timestamps:
00:00 - Lab overview knowledge
06:05 - Hands-On Lab
Prerequisites: Completed Azure AI-102 [4]: Responsible AI | Content Filters & Safety | Prevent Harmful AI Responses
Next in Series: LAB 6 - Explore AI Agent Development
#AIEvaluation #ModelTesting #AI102 #PerformanceMetrics