Learn to build and deploy semantic search systems using vector databases. This course covers vector embeddings, Approximate Nearest Neighbor (ANN) algorithms, database architecture, and practical implementation with popular tools. Develop the skills needed to create applications that understand meaning, not just keywords.
Prerequisites Basic Python, Data Concepts
Level:
Vector Embeddings
Understand the generation and application of various vector embedding models for different data types.
Vector Database Architecture
Explain the core components and design principles of modern vector databases.
ANN Algorithms
Compare and contrast different Approximate Nearest Neighbor search algorithms and their trade-offs.
Semantic Search Pipelines
Design and construct pipelines for indexing data and performing semantic queries.
Practical Implementation
Utilize popular vector database libraries and platforms to build a functional semantic search application.
Performance Tuning
Evaluate and tune indexing parameters for optimal search performance and resource usage.