Join us on Thursday, October 17th, for a Seattle Elastic User Group meetup. Andrew Dieken (Software Engineer at Guidebook) and Justilla Castilla (Sr. Developer Advocate at Elastic) will present, followed by networking, pizza, and refreshments.
Date and time:
Thursday, October 17th, from 5:30 – 7:30 pm PDT
Location:
Redmond Microsoft Reactor – 3709 157th Ave NE, Redmond, WA 98052
Parking:
Parking is free at Microsoft Building 20, the Reactor space (with overflow parking at Building 25).
Arrival Instructions:
Attendees will sign in at the front desk with their first and last names. A Reactor team member will be at the desk to assist with any questions.
Agenda:
5:30 pm: Doors open; say hi, grab a seat, and eat some food.
6:00 pm: Nailing User Email Search with Elasticsearch – Andrew Dieken (Guidebook)
6:30 pm: Elasticsearch is for the Birds: Identifying Feathered Friend Embedding Images as Vector Similarity Search – Justilla Castilla (Sr. Developer Advocate at Elastic)
7:00-7:30 pm: Networking & refreshments
7:30 pm: Event ends
Talk Abstracts:
Nailing User Email Search with Elasticsearch – Andrew Dieken (Guidebook)
In this talk, we will explore how to leverage both built-in and custom Elasticsearch analyzers and token filters to support diverse email search criteria. These include full email addresses, email domains, and partial email addresses for autocomplete functionality.
Elasticsearch is for the Birds: Identifying Feathered Friend Embedding Images as Vector Similarity Search – Justilla Castilla (Sr. Developer Advocate at Elastic)
Search no longer has to be a traditional term / frequency-inverse document frequency slog. The current trend of machine learning and models has opened another dimension for search, quite literally. In this talk we’ll cover “classic” search and its inherent limitations, what a model is and how you can use it.
Next, we’ll look at how to perform vector search or hybrid search using images of birds in Elasticsearch, generate embeddings from images and then use the techniques we learned to propose the most probable similar images after uploading our own image. We’ll close the talk with an overview of various enhancements to increase the performance and usability of your searches.
