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Course Outline
Introduction to Vector Databases
- Comprehending vector databases
- The role of Pinecone in AI applications
- Advantages over traditional database systems
Semantic Search with Pinecone
- Foundational principles of semantic search
- Configuring Pinecone for text-based searches
- Enhancing search results using vector embeddings
Product and Multi-modal Search
- Techniques for precise product recommendations
- Integrating text and image data for comprehensive search
- Case studies, including e-commerce applications
Conversational AI and Content Generation
- Enhancing chatbots through vector search
- The role of vector databases in text and image generation
- Building a basic Q&A bot
Security and Personalisation
- Utilising vector databases for anomaly and fraud detection
- Tailoring user experiences with vector data
- Personalisation strategies for media platforms
Scalability and Performance Optimisation
- Challenges associated with scaling vector databases
- Leveraging Pinecone’s serverless architecture for performance
- Key metrics for monitoring and optimising vector databases
Implementing Pinecone in AI
- Developing a vector database solution
- Session review and feedback
Requirements
- A foundational understanding of databases
- Introductory knowledge of AI and machine learning concepts
- Familiarity with core programming concepts
Audience
- Data scientists
- Software developers
- Machine learning enthusiasts
21 Hours
