Innovating with MongoDB Atlas Vector Search

Welcome to our campaign in collaboration with MongoDB, where we're set to deep dive into the fascinating world of Atlas Vector Search. Atlas Vector Search is a powerful feature within MongoDB Atlas, enabling efficient and sophisticated searches on high-dimensional vector data, a crucial capability in modern, data-driven applications.

Learning Atlas Vector Search is essential as it opens up new possibilities in handling and querying complex data types, making it a valuable skill in the evolving landscape of data management and AI-driven applications.

As we progress through this campaign, we'll not only learn the ins and outs of Atlas Vector Search but also apply our newfound skills to build a movie recommender project from scratch. This hands-on project will solidify our understanding and demonstrate the practical applications of our learning.

Additionally, we'll explore how Atlas Vector Search can be integrated into a Retrieval-Augmented Generation (RAG) architecture. RAG is an innovative approach that combines information retrieval with generative models to enhance output quality, particularly useful in tasks like content creation and question answering. This campaign is your gateway to mastering Atlas Vector Search and understanding its pivotal role in modern data science workflows.

By the end of this campaign, you will be able to:

• Describe MongoDB, MongoDB Atlas and Atlas Vector Search
• Utilize Atlas Vector Search
• Integrate third party libraries with Atlas Vector Search
• Build a movie recommender project
• Use Atlas Vector Search in a RAG model