Mastering Vector Databases for Scalable AI Solutions

• Basic knowledge of databases and data structures
• Familiarity with machine learning concepts
• Experience with a programming language (preferably Python)

Audience

• Data scientists
• Machine learning engineers
• Software developers
• Database administrators

A vector database is a specialized repository designed to store, index, and query high-dimensional vector data efficiently, facilitating advanced search and retrieval operations for AI applications.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level data scientists and machine learning engineers who wish to master the use of vector databases for scalable, high-performance AI applications.

By the end of this training, participants will be able to:

• Understand the principles of vector databases.
• Learn techniques for creating and managing vector embeddings.
• Explore indexing strategies for high-dimensional data.
• Develop skills for performing efficient similarity searches.
• Apply vector database knowledge to machine learning projects.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction

• What are vector databases?
• Vector databases vs traditional databases
• Overview of vector embeddings

Generating Vector Embeddings

• Techniques for creating embeddings from various data types
• Tools and libraries for embedding generation
• Best practices for embedding quality and dimensionality

Indexing and Retrieval in Vector Databases

• Indexing strategies for vector databases
• Building and optimizing indices for performance
• Similarity search algorithms and their applications

Vector Databases in Machine Learning (ML)

• Integrating vector databases with ML models
• Troubleshooting common issues when integrating vector databases with ML models
• Use cases: recommendation systems, image retrieval, NLP
• Case studies: successful implementations of vector databases

Scalability and Performance

• Challenges in scaling vector databases
• Techniques for distributed vector databases
• Performance metrics and monitoring

Project Work and Case Studies

• Hands-on project: Implementing a vector database solution
• Review of cutting-edge research and applications
• Group presentations and feedback

Summary and Next Steps