Self-Correcting ML + Multimodal Search with Elastic
About the event
Join the Elastic Washington, D.C. User Group on Wednesday, September 16th for an exciting meetup.
We’ll feature a presentation from Kritika Berry and Josh Phifer, followed by networking, refreshments, and pizza with the DC tech and Elastic community.
Date and Time: Wednesday, September 16th, from 5:30-7:30 pm EDT
Location: Elastic Arlington Office - 4100 Fairfax Drive, Ste 500, Arlington, VA 22203
Parking:
• The building’s parking garage is operated by Colonial Parking and is located off N. Randolph Street • Book a spot on[ SpotHero](https://spothero.com/search?kind=address&latitude=38.8818514&longitude=-77.1095268&search_string=4100+Fairfax+Dr+%23500%2C+Arlington%2C+VA+22203%2C+USA) • A Metro Station is located across the street
Agenda:
• 5:30 pm: Doors open; say hi, grab a seat, and eat some food. • 6:00 pm: Dashboards to Decisions: A Self-Correcting ML Loop with Elastic Observability - Kritika Berry • 6:40 pm: Building Multimodal Search with Elastic and Jina - Josh Phifer, Principal Solutions Architect at Elastic • 7:20-7:30 pm: Networking & refreshments
Talk Abstracts: Dashboards to Decisions: A Self-Correcting ML Loop with Elastic Observability - Kritika Berry (Software Engineer) *Most ML pipelines face a drop in accuracy or a spike in latency as it gradually trains which goes unnoticed until someone downstream complains. In this talk I’ll show how I wired Elastic Observability into my ML workflow so the pipeline watches itself: logs, metrics, and traces feed back into the system to catch drift and errors, then trigger corrective action automatically. This session delivers a practical pattern for turning observability data from something you just look at into something that actually fixes your models.*
Building Multimodal Search with Elastic and Jina Josh Phifer, Principal Solutions Architect at Elastic
*We’ll walk through two demos using Elastic and Jina to power multimodal search.* *The first is an artwork search experience where users can search with text, images, or a combination of both. From there, an agent can help narrow the results, compare pieces, and explore similarities across style, subject matter, color, and other visual details.* *The second demo applies the same concepts to video. Videos are divided into searchable segments, making it possible to find specific scenes, objects, actions, or topics with a simple question. The agent can search across clips, follow related results, and take users directly to the moments that matter.* *We’ll wrap up with a look at how both applications were built, what worked well, and some of the implementation details and tradeoffs behind each approach.*
