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Overview for airbnb

Applying Deep Learning To Airbnb Search

This discussion is about the use of machine learning at Airbnb. It's a success story about how machine learning helps Airbnb's search ranking to find guests the best possible options while rewarding the most deserving hosts. Ranking at Airbnb is a quest to understand the needs of the guests and the quality of the hosts to strike the best match possible. The talk discusses the work done in applying neural networks in an attempt to break out of that plateau and also focuses on the elements found useful in applying neural networks to a real life product. 


Bighead: Airbnb’s End-to-End Machine Learning Platform

Airbnb has a wide variety of ML problems ranging from models on traditional structured data to models built on unstructured data such as user reviews, messages and listing images. The ability to build, iterate on, and maintain healthy machine learning models is critical to Airbnb’s success. Many ML Platforms cover data collection, feature engineering, training, deploying, productionalization, and monitoring but few, if any, do all of the above seamlessly.


Zipline: Airbnb’s Machine Learning Data Management Platform

Zipline is Airbnb’s data management platform specifically designed for ML use cases. Previously, ML practitioners at Airbnb spent roughly 60% of their time on collecting and writing transformations for machine learning tasks. Zipline reduces this task from months to days. It allows users to define features in an easy-to-use configuration language, then provides access to the following features: resource efficient and point-in-time correct training set backfills and scheduled updates, feature visualizations and automatic data quality monitoring, feature availability in online scoring environment: batch and streaming with batch correction (lambda architecture), collaboration and sharing of features, and data ownership and management.

Spark powers many of Zipline’s features, especially offline tasks for efficient training set backfills and feature computation. This discussion covers Ziplines architecture and the main problems that Zipline solves. Despite being widespread, there is no open source software to address these problems.


Airbnb: Driving a Higher Level of Customer Support with Machine Learning

At Airbnb, Machine Learning (ML) and Natural Language Processing (NLP) hold the promise to facilitate, optimize and improve customer experiences. Such applications range from understanding customer feedbacks and issues to providing a more reachable and efficient service in order to resolve the issues more effectively. In this talk, Yashar Mehdad, a Data Scientist Manager at Airbnbwill highlights various ways in which ML and NLP techniques are used in supporting their customers.


Building Data Product Based on Apache Spark at Airbnb

Building data product requires having Lambda Architecture to bridge the batch and streaming processing. AirStream is a framework built on top of Apache Spark to allow users to easily build data products at Airbnb. It proved Spark is impactful and useful in the production for mission-critical data products.


Using Apache Airflow as a platform for data engineering frameworks

Learn how Airbnb uses Airflow which has ability to dynamically generate pipelines to power frameworks addressing the needs of the data teams. This discussion also explores some of Airflow expressiveness via a couple of examples running in production at Airbnb.