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.<br /><br />🔍 What You’ll Learn in This Video:<br />1️⃣ Key Metrics for Evaluating Generative AI Performance<br />2️⃣ Understanding Model Accuracy, Bias, and Responsiveness<br />3️⃣ Using Azure AI Foundry for AI Model Testing<br />4️⃣ Evaluating Text Quality, Coherence, and Relevance<br />5️⃣ Performance Benchmarking: Latency, Cost, and Scalability<br />6️⃣ Best Practices for Optimizing AI Model Outputs<br /><br />🛠️ Who Is This For?<br />AI & ML Enthusiasts looking to optimize AI models<br />Developers & data scientists working with LLMs & generative AI<br />Professionals preparing for the Microsoft AI-900 Certification<br />Businesses seeking reliable AI solutions for real-world applications<br /><br />📌 Key Highlights:<br />✅ Hands-on demo of AI performance evaluation techniques<br />✅ How to assess AI-generated content for quality & bias<br />✅ Using Azure AI tools for testing & optimizing generative AI<br />✅ Best practices for improving AI efficiency & cost-effectiveness<br /><br />💡 Learn how to build safer AI applications with Azure AI Foundry today!<br /><br />Explore Our other Courses and Additional Resources on: https://www.youtube.com/@skilltechclub
