AI-900 LAB: Evaluating Generative AI Performance in Azure AI
Welcome to this AI-900 lab session, where we explore how to evaluate the performance of generative AI models using Azure AI. With the rapid advancements in large language models (LLMs) and AI-driven applications, it’s crucial to measure and optimize their accuracy, efficiency, and reliability. This hands-on tutorial will guide you through various techniques to analyze, test, and improve generative AI models in Azure AI Foundry.
🔍 What You’ll Learn in This Video:
1️⃣ Key Metrics for Evaluating Generative AI Performance
2️⃣ Understanding Model Accuracy, Bias, and Responsiveness
3️⃣ Using Azure AI Foundry for AI Model Testing
4️⃣ Evaluating Text Quality, Coherence, and Relevance
5️⃣ Performance Benchmarking: Latency, Cost, and Scalability
6️⃣ Best Practices for Optimizing AI Model Outputs
🛠️ Who Is This For?
AI & ML Enthusiasts looking to optimize AI models
Developers & data scientists working with LLMs & generative AI
Professionals preparing for the Microsoft AI-900 Certification
Businesses seeking reliable AI solutions for real-world applications
📌 Key Highlights:
✅ Hands-on demo of AI performance evaluation techniques
✅ How to assess AI-generated content for quality & bias
✅ Using Azure AI tools for testing & optimizing generative AI
✅ Best practices for improving AI efficiency & cost-effectiveness
💡 Learn how to build safer AI applications with Azure AI Foundry today!
🔗 Additional Resources:
👉 Azure AI Foundry Documentation:https://microsoftlearning.github.io/m...
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Chapters:
00:00 - 00:54 - Intro
00:54 - 03:33 - Create an AI hub and project in the Azure AI Foundry
03:33 - 06:54 - Deploy a model
06:54 - 13:40 - Manually evaluate a language model in the Azure AI Foundry portal
13:40 - 20:52 - Evaluate your chat app with the built-in metrics
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