Vector Search with NoSQL Databases using MongoDB & Cassandra

The vector database market is set to grow at a 20% CAGR by 2032 (Global Market Insights). This course gives data scientists, ML engineers, GenAI engineers, and software developers the sought-after skills for performing vector searches in NoSQL databases. Businesses carry out vector searches in NoSQL databases to improve an AI model's search accuracy and efficiency. During this micro course, you'll learn how to store and index vectors in MongoDB, perform vector searches, and apply the techniques in text similarity analysis and building image classification systems. Plus, you'll look at Cassandra, its features for storing and querying vectors, and how to carry out vector searches. You'll also examine how to apply these concepts to building applications for movie recommendation, inventory management, and personalization. Plus, you'll get valuable practice applying your knowledge through hands-on labs and a real-world final project. Note that this micro course is part of the Vector Database Fundamentals specialization, which is ideal for professionals who work with vector databases, relational databases, and NoSQL databases for AI. It requires a basic knowledge of MongoDB, Cassandra, and Node.js.

• Vector Search Implementation in NoSQL Databases
• Welcome to this module, where you’ll explore integrating vector search capabilities with NoSQL databases such as MongoDB and Cassandra. You’ll explore the fundamentals of MongoDB and Cassandra, its features for storing and querying vectors, and how to perform efficient vector searches. You’ll also learn how to leverage the unique features of each database system to perform efficient vector searches and build practical applications such as text similarity analysis and recommendation systems.