Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Introduction
- Defining vector databases
- Comparing vector databases with traditional databases
- An overview of vector embeddings
Generating Vector Embeddings
- Methods for creating embeddings from diverse data types
- Tools and libraries utilized for embedding generation
- Best practices regarding embedding quality and dimensionality
Indexing and Retrieval in Vector Databases
- Strategies for indexing within vector databases
- Constructing and optimizing indices for peak performance
- Similarity search algorithms and their real-world applications
Vector Databases in Machine Learning (ML)
- Integrating vector databases with ML models
- Troubleshooting common integration challenges between vector databases and ML models
- Key use cases: recommendation systems, image retrieval, and NLP
- Case studies highlighting successful vector database implementations
Scalability and Performance
- Challenges associated with scaling vector databases
- Techniques for implementing distributed vector databases
- Performance metrics and monitoring practices
Project Work and Case Studies
- Hands-on project: Building a complete vector database solution
- Review of cutting-edge research and emerging applications
- Group presentations and constructive feedback
Summary and Next Steps
Requirements
- Foundational understanding of databases and data structures
- Basic familiarity with machine learning concepts
- Proficiency in a programming language, preferably Python
Target Audience
- Data scientists
- Machine learning engineers
- Software developers
- Database administrators
