DeepAI AI Chat
Log In Sign Up

Deploying Deep Ranking Models for Search Verticals

06/06/2018
by   Rohan Ramanath, et al.
0

In this paper, we present an architecture executing a complex machine learning model such as a neural network capturing semantic similarity between a query and a document; and deploy to a real-world production system serving 500M+users. We present the challenges that arise in a real-world system and how we solve them. We demonstrate that our architecture provides competitive modeling capability without any significant performance impact to the system in terms of latency. Our modular solution and insights can be used by other real-world search systems to realize and productionize recent gains in neural networks.

READ FULL TEXT

page 1

page 2

page 3

page 4

07/11/2020

Optimizing Prediction Serving on Low-Latency Serverless Dataflow

Prediction serving systems are designed to provide large volumes of low-...
12/12/2020

Efficient Incorporation of Multiple Latency Targets in the Once-For-All Network

Neural Architecture Search has proven an effective method of automating ...
11/18/2020

Challenges in Deploying Machine Learning: a Survey of Case Studies

In recent years, machine learning has received increased interest both a...
08/06/2020

DeText: A Deep Text Ranking Framework with BERT

Ranking is the most important component in a search system. Mostsearch s...
10/22/2018

Applying Deep Learning To Airbnb Search

The application to search ranking is one of the biggest machine learning...
06/06/2022

Is a Modular Architecture Enough?

Inspired from human cognition, machine learning systems are gradually re...
06/06/2017

Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric Approach

A significant amount of search queries originate from some real world in...