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Plan Szkolenia
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
Wymagania
- 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
14 godzin
