Course Outline

Introduction

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

Generating Vector Embeddings

  • Methods for creating embeddings from diverse data types
  • Tools and libraries used for embedding generation
  • Best practices for ensuring high-quality and appropriate dimensionality of embeddings

Indexing and Retrieval in Vector Databases

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

Vector Databases in Machine Learning (ML)

  • Integration of vector databases with ML models for government
  • Common issues and solutions when integrating vector databases with ML models
  • Use cases: recommendation systems, image retrieval, natural language processing (NLP)
  • Case studies of successful implementations of vector databases

Scalability and Performance

  • Challenges in scaling vector databases for government use
  • Techniques for distributed vector databases to support large-scale operations
  • Performance metrics and monitoring strategies

Project Work and Case Studies

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

Summary and Next Steps

Requirements

  • Fundamental understanding of databases and data structures
  • Knowledge of machine learning principles
  • Proficiency in a programming language (preferably Python)

Audience

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

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