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Course Outline
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
Requirements
- 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 Hours
Delivery Options
Private Group Training
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- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from €4560 online delivery, based on a group of 2 delegates, €1440 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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