Building End-to-End Recommender Systems with Nvidia Merlin

Sep 27, 10:00AM PST(05:00PM GMT).
  • Virtual AICamp
  • Free 222 Attendees

Industrial recommender systems (RecSys) are made up of complex pipelines requiring multiple steps including data preprocessing and feature engineering, building and training recommender models and deploying them into production. Data Scientists and ML engineers might focus on different stages of recommender systems, however they share a common desire to reduce the time and effort searching for and combining boilerplate code coming from different sources or writing custom code from scratch to create their own RecSys pipelines.

In this presentation, we will first touch upon main challenges of building and deploying RecSys, and then introduce NVIDIA Merlin framework that consists of a set of libraries and tools to help RecSys practitioners build models and pipelines more easily and efficiently. We will conclude the presentation with a short demo of building and training recommender models with Merlin Models library, a component of the Merlin framework, designed to simplify the development and even deployment of recommender models.

Lucky draw prizes

We will raffle 5 winners for the book during the event. To enter the lucky draw, please complete one of the two steps:
- Twitter the event with hashtag #recaicamp and tag @aicampai . for example:

#recaicamp online AI/ML tech talk series by @aicampai: @ak_ronay and Benedikt will discuss building end-to-end recommender systems with Nvidia Merlin @NVIDIAAI. Free RSVP:
- Commend or reshare the post on linkedin: LinkedIn Post

* print copy for winners in US and e-book for winners outside US. This tech talk event is sponsored by Packt.
Ronay Ak (Nvidia)

Sr. Data Scientist at NVIDIA working on recommender systems. Before joining NVIDIA, she worked as a Research Associate at the National Institute of Standards and Technology in MD, USA. She received her Ph.D. in Energy & Power Systems (Engineering) discipline from CentraleSupelec in Paris, FR. She was part of the teams that won past RecSys competitions: WSDM WebTour Workshop Challenge 2021 by and the SIGIR eCommerceWorkshop Data Challenge 2021 by Coveo. She has authored more than 20 technical publications published in internationally reputed conferences and journals.
Benedikt Schifferer
DL engineer at NVIDIA working on recommender systems. Before joining NVIDIA, he worked as a data scientist and developed the in-house recommendation engine at Home24, an ecommerce store for furniture. Afterwards, he worked as a data science consultant at McKinsey & Company, presenting the implemented machine learning solutions to technical and non-technical audiences. He got his Msc degree from Columbia University, New York, where Benedikt was a teaching assistant for two courses, Applied Deep Learning and Deep Learning. Benedikt was part of the winning team of the ACM RecSys2020, RecSys2021 challenges and WSDM2021 challenge.
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