Скачать с ютуб AI-900 LAB: Fine-Tuning GPT Models in Azure AI Foundry – Step-by-Step Guide в хорошем качестве

AI-900 LAB: Fine-Tuning GPT Models in Azure AI Foundry – Step-by-Step Guide

Welcome to this hands-on AI-900 lab session, where we dive deep into fine-tuning GPT models using Azure AI Foundry! Fine-tuning allows developers to customize AI models for specific industries, use cases, and data sets, improving response accuracy and relevance. Whether you're preparing for the Microsoft AI-900 Certification or working on AI-driven applications, this tutorial will provide a step-by-step walkthrough of fine-tuning GPT models in Azure. 🔍 What You’ll Learn in This Video: 1️⃣ Introduction to Fine-Tuning GPT Models in Azure AI Foundry 2️⃣ Why Fine-Tuning is Essential for Custom AI Applications 3️⃣ Setting Up Azure AI Foundry for GPT Model Fine-Tuning 4️⃣ Uploading & Preparing Training Data for Customization 5️⃣ Training and Deploying a Fine-Tuned GPT Model 6️⃣ Testing & Optimizing Model Performance for Real-World Use Cases 🛠️ Who Is This For? AI & ML Enthusiasts exploring custom AI solutions Developers & data scientists working on AI-powered applications Professionals preparing for the Microsoft AI-900 Certification Businesses looking to customize AI for customer support, automation, & NLP 📌 Key Highlights: ✅ Hands-on demo of GPT fine-tuning in Azure AI Foundry ✅ Customizing AI models for industry-specific applications ✅ Training and deploying AI models with real-world datasets ✅ Optimizing GPT models for better accuracy & efficiency 💡 Learn how to fine-tune AI models for better, more tailored responses using Azure AI Foundry today! 🔗 Additional Resources: 👉 Azure AI Foundry Documentation:https://microsoftlearning.github.io/m... 👍 Found this tutorial helpful? Don’t forget to like, share, and subscribe for more Azure AI insights! Chapters: 0:00 - 01:24 - Intro 01:24 - 04:45 - Create an AI hub and project in the Azure AI Foundry portal 04:45 - 13:50 - Fine-tune a GPT-3.5 model 13:50 - 19:14 - Chat with a base model 19:14 - 22:37 - Deploy the fine-tuned model 22:37 - 25:11 - Test the fine-tuned model