This course is a comprehensive guide to building advanced search engines and vector databases using Vespa AI and Python. It is designed for data scientists, software developers, AI enthusiasts, and anyone interested in mastering modern search technologies. Throughout this course, you will learn the fundamentals of Vespa AI, including its architecture and core components, and how to leverage its capabilities to build high-performance search applications.
You will gain hands-on experience with Python to integrate Vespa AI for real-time data processing, ranking, and retrieval. The course covers essential topics such as developing and deploying vector databases, creating scalable search engines, and using machine learning models to enhance search results. Additionally, you will explore advanced search techniques like semantic search, approximate nearest neighbor search, and hybrid search methods.
The course includes practical projects that guide you through deploying applications on Vespa Cloud, optimizing search performance with custom ranking functions, and implementing filters and cross-hit normalization for better search accuracy. By the end of this course, you will have the skills to create and deploy powerful, scalable search applications and vector databases.
Prerequisites include a basic understanding of Python and familiarity with Google Colab. This course provides valuable insights and practical experience to advance your knowledge in search technologies and AI integration.
Source code is provided in sections.
