AI / Deep Learning | Data Science |

NVIDIA Deepens Commitment to Streamlining Recommender Workflows with GTC Spring Sessions

Ensuring recommenders are meaningful, personalized, and relevant to a single customer is not easy. Scaling a personalized recommender experience to hundreds of thousands, or millions of customers, comes with unique challenges that data scientists and machine learning engineers tackle every day. Scaling challenges often provide obstacles to effective ETL, training, retraining, or deploying models into production.

To tackle these challenges, machine learning engineers and data scientists within the industry utilize a combination, or hybrid of tools, techniques, and algorithms. NVIDIA is committed to help streamline recommender workflows with Merlin, an open-source framework that is interoperable and designed to support machine learning engineers and data scientists with preprocessing, feature engineering, training, and inference. Merlin supports industry leaders who are tackling common recommender challenges as they provide relevant, impactful, and fresh recommenders at scale.

Here a few key sessions from industry leaders in media, delivery-on-demand, and retail at GTC Spring 2021.

  • AI-First Social Media Feeds: A View From the Trenches
    ShareChat discusses efficient training of extremely large recommender models with billions of parameters distributed across multiple GPUs and workers as well as the importance of continual model updates in near-real time to deal with the key challenge of concept drift. The session includes how solutions in NVIDIA’s Merlin stack resolve key bottlenecks faced in general purpose deep learning frameworks.

Registration is free, visit the GTC website for more information.