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Course Outline
Introduction to Vector Databases
- Understanding vector databases
- Pinecone's role in AI applications
- Benefits over traditional databases
Semantic Search with Pinecone
- Principles of semantic search
- Setting up Pinecone for text-based searches
- Enhancing search results with vector embeddings
Product and Multi-modal Search
- Techniques for accurate product recommendations
- Combining text and image data for comprehensive search
- Case studies (e.g. e-commerce applications)
Conversational AI and Content Generation
- Improving chatbots with vector search
- Vector databases in text and image generation
- Building a simple Q&A bot
Security and Personalization
- Vector databases in anomaly and fraud detection
- Personalizing user experiences with vector data
- Personalization in media platforms
Scalability and Performance Optimization
- Challenges in scaling vector databases
- Pinecone's serverless architecture for performance
- Metrics for monitoring and optimizing vector databases
Implementing Pinecone in AI
- Developing a vector database solution
- Review and feedback
Summary and Next Steps
Requirements
- Basic understanding of databases
- Introductory knowledge of AI and machine learning concepts
- Familiarity with programming concepts
Audience
- Data scientists
- Software developers
- Machine learning enthusiasts
21 Hours