Dive deep into advanced vector search techniques with this specialized course on Temporal Similarity Search (TSS) and Hybrid Search using KDB AI. This course is designed for those who want to take their vector database skills to the next level.
In this course, you'll gain hands-on experience with:
Temporal Similarity Search fundamentals and applications
Implementing TSS for time-series data analysis
Hybrid Search techniques combining dense and sparse vector search
Optimizing search performance for complex data scenarios
Through practical examples and real-world use cases, you'll learn to:
Apply TSS to uncover patterns in time-dependent datasets
Implement both transformed and non-transformed TSS
Leverage Hybrid Search to improve search accuracy and relevance for unstructured data retrieval
Optimize your search pipelines for multi-modal data retrieval
Real-time anomaly detection with non-transformed TSS
By the end of this course, you'll have the advanced skills to implement cutting-edge search solutions for time-series data and complex multi-modal datasets, opening up new possibilities in finance, IoT, and other time-sensitive domains.
This course is ideal for developers and data scientists who already have a basic understanding of vector databases and want to expand their expertise. Join me to master these advanced techniques and stay at the forefront of vector search technology!
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Michael Ryaboy is an AI Developer Advocate at KX, specializing in vector databases and full-stack AI applications. With expertise in crafting sophisticated RAG solutions, similarity search at scale, and data engineering, he creates content and resources for developers. Michael is passionate about fostering community collaboration and pushing the boundaries of AI technology.
