11 Understanding embeddings in Azure OpenAI Service

Description

Explore the concept of embeddings in the Azure OpenAI Service. This session explains what embeddings are, why they matter, and how they enable machine learning models to represent text as vector data facilitating semantic search, similarity, and code search. Learn about Microsoft’s embedding models such as text-embedding-3-large, text-embedding-3-small, and text-embedding-ada-002, including their uses and capabilities. *********Contact me for Voucher: ************ Telegram ID: https://t.me/AICloudGen Telegram Group: https://t.me/microsoftcert Mail: cloudvis1on@outlook.com ⏰Timestamp⏰ 00:00 Introduction to embeddings 00:11 What embeddings represent in machine learning 00:34 Vector representation and semantic similarity 00:57 Overview of Microsoft embedding models 01:30 Differences between text embedding models 02:20 What embeddings enable: similarity, search, and code search 03:15 How embedding vectors are used with cosine similarity 04:10 Explaining cosine similarity mathematics 05:00 Generating embeddings using Azure OpenAI service 05:30 Example of curl command to generate embeddings 06:30 Example of C# code for generating embeddings 07:30 Creating and deploying embedding models in Azure 08:30 Summary and practical applications *********Contact me for Voucher: ************ Mail: cloudvis1on@outlook.com Telegram ID:t.me/AICloudGen Whatsapp Channel: https://tinyurl.com/AICloudgenGenCom Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.