Course Outline

Introduction

  • What are vector databases?
  • Comparison of vector databases to traditional databases
  • Overview of vector embeddings

Generating Vector Embeddings

  • Techniques for creating embeddings from diverse data types
  • Tools and libraries for generating embeddings
  • Best practices for ensuring embedding quality and dimensionality

Indexing and Retrieval in Vector Databases

  • Indexing strategies specific to vector databases
  • Methods for building and optimizing indices to enhance performance
  • Similarity search algorithms and their practical applications

Vector Databases in Machine Learning (ML)

  • Integrating vector databases with machine learning models
  • Addressing common challenges when integrating vector databases with ML models
  • Use cases: recommendation systems, image retrieval, natural language processing
  • Case studies highlighting successful implementations of vector databases for government and industry

Scalability and Performance

  • Challenges in scaling vector databases for government operations
  • Techniques for implementing distributed vector databases
  • Key performance metrics and monitoring strategies

Project Work and Case Studies

  • Hands-on project: Implementing a vector database solution for government applications
  • Review of cutting-edge research and real-world applications in the public sector
  • Group presentations and peer feedback sessions

Summary and Next Steps

Requirements

  • Basic understanding of databases and data structures
  • Knowledge of machine learning principles
  • Experience with a programming language, preferably Python

Audience for Government

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

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