Efficient Vector Search with OpenSearch

Описание: TV/Film, ad creatives, and even DJs can use some help finding the perfect song. Can we solve such an intricate problem with Machine Learning and AWS OpenSearch? Absolutely!

Hosted by tech legend Kris Jenkins, hear from Itamar Syn-Hershko, CTO and Founder of BigData Boutique, and Matan Kollenscher, CEO and Chief Architect at MyPart, and Itamar Syn-Hershko, CTO and Founder at BigData Boutique, to learn how MyPart revolutionized song search using Vector Embedding and OpenSearch.

In this session, we discuss MyPart's unique use-case and multi-dimensional approach that slices up sounds, lyrics, and musical compositions into vectors that make sense to a computer. With that information stored in a Vector Database, MyPart can run intelligent, low-latency queries on huge music catalogs, without compromising accuracy or functionality.

Then we dive into the intricacies of Vector Search with OpenSearch, learning ML techniques that apply to a wide range of projects. Going deep into the technology, we discuss how OpenSearch makes searching through vector data easy and efficient:

Vector embeddings and vector search algorithms
Use cases for Vector Search
OpenSearch and its vector search features
Common bottlenecks and optimizations

This webinar will help you understand how AI can help your business, how KNN navigates a vector database, and just how technology can make a science out of crate digging.

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Ready to explore further? Check out these useful links from the webinar:
🔗 MyPart Song Search: https://www.mypart.net/
🔗 Contact BigData Boutique: https://bigdataboutique.com/services/...
🔗 Pulse for OpenSearch: https://bigdataboutique.com/solutions...

Plus, dive deeper into the resources mentioned:
📚 Hugging Face - Pre-trained embedding models: https://huggingface.co/
📚 Get started with ANN on OpenSearch: https://opensearch.org/docs/latest/se...
📚 k-NN Index Documentation: https://opensearch.org/docs/latest/se...
📚 Amazon OpenSearch Service’s vector database capabilities explained: https://aws.amazon.com/blogs/big-data...
📚 Choosing k-NN algorithm: https://aws.amazon.com/blogs/big-data...