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Duration 14 hours
Course Outline
Introduction
- Defining vector databases.
- Comparing vector databases with traditional databases.
- Overview of vector embeddings.
Generating Vector Embeddings
- Techniques for creating embeddings from various data types.
- Tools and libraries used for embedding generation.
- Best practices for ensuring embedding quality and managing dimensionality.
Indexing and Retrieval in Vector Databases
- Indexing strategies specific to vector databases.
- Building and optimizing indices to enhance performance.
- Similarity search algorithms and their practical 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 involved in scaling vector databases.
- Techniques for implementing 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
Requirements
- Foundational knowledge of databases and data structures.
- Familiarity with core machine learning concepts.
- Practical experience with a programming language, preferably Python.
Audience
- Data scientists.
- Machine learning engineers.
- Software developers.
- Database administrators.
