Filter (clear filters)





Overview for machine-learning

Kubernetizing Big Data and ML Workloads at Uber - Mayank Bansal & Min Cai, Uber

Uber relies on Big Data and ML to make business critical decisions such as pricing, trip ETA, etc. Today, those workloads such as Hive and Spark are running on YARN. To save millions of dollars by efficient use of cluster resources, Uber is planning to use Kubernetes to co-locate BigData/ML and micro-service workloads.

This talk will covers the following:
- Learnings of running large-scale BigData/ML on Kubernetes with Peloton
- Colocation of mixed workloads
- Federation across zones
- Feature and API parity with YARN


Best Practices for Prototyping Machine Learning Models for Healthcare

Lorenzo Rossi of City of Hope National Medical Center discusses supervised learning from the Electronic Health Records, covering cohort definition, data preparation and performance metrics. 


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. 


How Malwarebytes Leverages Big Data, AI

In this talk Sujay Kulkarni Manager, Data Engineering at Malwarebytes discusses how Malwarebytes leverages big data, AI and the cloud to protect millions of companies and individuals globally from the world’s most harmful internet threats and malware. They collect billions of records each day on a millisecond by millisecond basis, and apply advanced machine learning and AI to identify and profile harmful threats and their sources. 


Collaborative Metric Learning Recommendation System: Application to Theatrical Movie Releases

Miguel Angel Campo highlights how the data team from Century Fox developed a system based on Collaborative (Deep) Metric Learning (CML) to predict the purchase probabilities of new theatrical releases. He explaind how they trained and evaluated the model using a large dataset of customer histories spanning multiple years, and tested the model for a set of movies that were released outside of the training window. Initial experiments show gains relative to models that don't train on collaborative preferences.