AI-900 Azure AI Foundry with Prompt Flow for Named Entity Recognition
Welcome to this hands-on AI-900 lab session, where we explore how Azure AI Foundry combined with Prompt Flow enables Named Entity Recognition (NER)—a key Natural Language Processing (NLP) technique used for extracting specific entities like names, locations, dates, and more from text. Whether you're preparing for the Microsoft AI-900 Certification or looking to enhance AI-driven text processing, this step-by-step demo will guide you through everything you need to know.
🔍 What You’ll Learn in This Video:
1️⃣ Introduction to Azure AI Foundry & Prompt Flow
2️⃣ Understanding Named Entity Recognition (NER) & its applications
3️⃣ Setting up Azure AI Foundry for NLP tasks
4️⃣ Building a Prompt Flow pipeline for entity extraction
5️⃣ Fine-tuning NER models for domain-specific applications
6️⃣ Deploying and testing NER models in real-world scenarios
🛠️ Who Is This For?
AI & ML Enthusiasts exploring NLP with Azure AI
Developers and data scientists working on AI-driven text processing
Professionals preparing for the Microsoft AI-900 Certification
Businesses looking to automate data extraction from unstructured text
📌 Key Highlights:
✅ Hands-on demo of Named Entity Recognition (NER)
✅ Building and optimizing Prompt Flow for AI-driven text processing
✅ Using Azure AI Foundry to train & deploy NLP models
✅ Real-world use cases in finance, healthcare, customer support & more
💡 Unlock the power of AI-driven text processing with Azure AI Foundry & Prompt Flow today!
🔗 Additional Resources:
👉 Azure AI Foundry Documentation: https://microsoftlearning.github.io/m...
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Chapters:
0:00 - 01:24 - Intro
01:24 - 05:13 - What is Prom flow?
05:13 - 08:57 - Create a project in the Azure AI Foundry portal
08:57 - 12:09 - Deploy a GPT model
12:09 - 19:33 - Create and run a flow in the Azure AI Foundry portal
19:33 - 23:27 - Configure the LLM node
23:27 - 29:42 - Configure the Python node
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