ML Research
Scalar Quantization Optimized for Vector Databases
Optimizing scalar quantization for the vector database use case allows us to achieve significantly better performance for the same retrieval quality at high compression ratios.
Evaluating RAG: A journey through metrics
Learn how Elastic is evaluating RAG.
Introducing Scalar Quantization in Lucene
How did we introduce scalar quantization into Lucene
Scalar quantization 101
What is scalar quantization and how does it work?
Improving information retrieval in the Elastic Stack: Improved inference performance with ELSER v2
Learn about the improvements we've made to the inference performance of ELSER v2.
Improving information retrieval in the Elastic Stack: Optimizing retrieval with ELSER v2
Learn about how we're reducing retrieval costs for ELSER v2.
Generative AI architectures with transformers explained from the ground up
This long-form article explains how generative AI works, from the ground all the way up to generative transformer architectures with a focus on intuitions.
Vector search in Elasticsearch: The rationale behind the design
There are different ways to implement a vector database, which have different trade-offs. In this blog, you'll learn more about how vector search has been integrated into Elastisearch and the trade-offs that we made.
.png&w=3840&q=100)
Open-sourcing sysgrok — An AI assistant for analyzing, understanding, and optimizing systems
Sysgrok is an experimental proof-of-concept, intended to demonstrate how LLMs can be used to help SWEs and SREs understand systems, debug issues, and optimize performance.