Master Temporal Similarity Search and Hybrid Search using KDB AI for powerful time-series and unstructured data
What you will learn
Data scientists with experience in vector databases looking to expand their skillset.
Software engineers working on search systems or time-series data analysis.
AI researchers interested in advanced retrieval techniques for large-scale datasets.
Financial analysts or quants working with time-series data in trading or risk management.
Why take this course?
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!