AI Semantic Search for E-commerce | Azure Cosmos DB
In this tutorial, you'll learn how to build low-latency recommendation engines with Azure Cosmos DB and Azure OpenAI Service. By using vector-based semantic search, you can offer personalized recommendations in real-time, going beyond traditional keyword limitations. With pre-trained models stored in Azure Cosmos DB, you can tailor product predictions based on user interactions and preferences. This tutorial also explores the power of augmented vector search to optimize results prioritized by relevance, building recommendation systems with limitless scalability, leveraging pre-computed vectors and collaborative filtering.
This tutorial is presented by Kirill Gavrylyuk, Azure Cosmos DB General Manager, and offers step-by-step guidance on how to create recommendation systems that deliver next-level, real-time insights. Whether you're a seasoned developer or just starting your journey, this tutorial provides you with the know-how to get the best results.
Make sure to check out the quick links and code snippets provided in the tutorial to get started. You can also try out Cosmos DB for MongoDB for free, and watch more Microsoft Mechanics videos to keep up with the latest in tech.
The post AI Semantic Search for Your Website with Azure Cosmos DB | E-commerce originally appeared on Microsoft Mechanics.
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