• Expedia Group Technology - Data Science Powering Vector Embedding Capabilities Empowering developers with seamless vector embedding solutions Introduction Rapid advances in Machine Learning (ML), especially Generative AI, have increased the need for specialized capabilities like vector embedding similarity search. • Vector embeddings are the numerical representations created by machine learning models which allow disparate inputs to be compared against each other. • A similarity search can be accomplished by querying an indexed collection of vectors for items similar to a given vector. • This process involves comparing the distance between the input vector - a single point in a multidimensional space - and each vector in the collection. • Techniques such as k-nearest neighbors search (KNN or NNS) and approximate nearest neighbors search (ANN) are often employed to efficiently identify vectors that are most similar to the input vector. • Vector similarity search is gaining increased attention, particularly due to the growth in the use of large language models (LLMs).
Article Summaries:
- Expedia Group’s Machine Learning Platform team has launched an Embedding Store Service to centralise the creation, storage, and querying of vector embeddings. The new service, built on the open‑source Feast feature store, offers CRUD operations, similarity search, and metadata filtering, enabling teams to manage embeddings from a single, consistent source. By linking embedding models to their data and enforcing schema contracts, the platform reduces engineering overhead, improves collaboration, and supports rapid iteration on ML‑powered services. The initiative reflects the growing demand for vector similarity search in generative AI workloads and aims to streamline deployment across Expedia’s data science teams.
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