You might have heard of vector search, but maybe are still wondering whether or not your company can benefit.
In this video, you'll learn why leading organizations are using vector search, powered by machine learning, to deliver a new digital experience.
When customers can't find information they need, frustration ensues. Your most motivated customers may not even know where to start. Without keywords, they are unable to search for what they mean, which can directly impact your bottom line.
Vector search ushers in a new era of search experience — where queries don't have to include specific keywords and semantic search applies to images, audio, and more. You can use it to unlock new ways of intelligent discovery, such as recommendation engines in ecommerce and question-answering using transformer models.
Walkthrough the core pieces needed to implement vector search with Elastic, which supports NLP, approximate nearest neighbor search, and importing pretrained PyTorch models. You can start with traditional retrieval techniques, apply vector search where it matters, and get the best of both worlds to exceed customer expectations.
- Why companies are looking at vector search
- Understanding the vector similarity algorithms
- Walkthrough what's needed to implement vector search within the Elastic Platform