Build search engines and vector databases with Vespa AI. Master Python integration, data processing, and ML techniques.
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.
What you’ll learn
- Understand Vespa AI: Learn the fundamentals of Vespa AI to build and deploy powerful search engines and vector databases effectively..
- Build Search Applications: Create advanced search applications with Vespa AI using Python, focusing on real-time data processing and retrieval..
- Develop Vector Databases: Learn to develop, deploy, and manage vector databases with Vespa AI, enhancing search with machine learning models..
- Integrate Vespa AI with Python: Gain practical skills to integrate Vespa AI into Python projects, from deploying applications to scaling for real-world use case.
Course Content
- Vespa Introduction –> 2 lectures • 7min.
- Vespa’s Overview and Architecture –> 2 lectures • 12min.
- Vespa Cloud and Tenant –> 3 lectures • 9min.
- Install and Load Dependencies | Google Colab | Python –> 2 lectures • 5min.
- Dataset and Convert to Vespa’s Format | Google Colab | Python –> 3 lectures • 7min.
- Application Package and Vespa Cloud Instance | Google Colab | Python –> 3 lectures • 10min.
- Deployment, Feed Data to Vespa Application | Google Colab | Python –> 3 lectures • 11min.
- All Search Options in Vespa | Google Colab | Python –> 5 lectures • 15min.
- Document Operations | Google Colab | Python –> 1 lecture • 4min.
- Reconnect with Vespa Application | Google Colab | Python –> 1 lecture • 2min.
Requirements
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.