NVIDIA showed how AI workflows can be leveraged to help you accelerate the development of AI solutions to address a range of use cases at NVIDIA GTC 2023. AI workflows are cloud-native, packaged reference examples showing how NVIDIA AI frameworks can be used to efficiently build AI solutions such as intelligent virtual assistants, digital fingerprinting for cybersecurity…
]]>Data scientists and machine learning engineers use many methods, techniques, and tools to prep, build, train, deploy, and optimize their machine learning models. While technical leads cite the importance of leveraging open source software for recommender team workflows, the majority of popular machine learning methods, libraries, and frameworks are not designed to support and accelerate…
]]>Recommender systems are the economic engine of the Internet. It is hard to imagine any other type of applications with more direct impact in our daily digital lives: Trillions of items to be recommended to billions of people. Recommender systems filter products and services among an overwhelming number of options, easing the paradox of choice that most users face. As the amount of data…
]]>The NVIDIA Merlin and KGMON team earned 1st place in the RecSys Challenge 2021 by effectively predicting the probability of user engagement within a dynamic environment and providing fair recommendations on a multi-million point dataset. Twitter sponsored the RecSys Challenge 2021, curated the challenge’s multi-goal optimization requirements to mirror the real world, and provided multi-million…
]]>Billions of people in the world are online. Many discrete moments online are spent browsing, shopping, streaming entertainment, or engaging with social media. Each discrete moment, or session, online is an opportunity for recommenders to make informed decisions a bit easier, faster, and more personalized for an individual person.Yet, when considering scale, this translates into recommenders…
]]>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…
]]>NVIDIA Merlin is an open beta application framework and ecosystem that enables the end-to-end development of recommender systems, from data preprocessing to model training and inference, all accelerated on NVIDIA GPU. We announced Merlin in a previous post and have been continuously making updates to the open beta. In this post, we detail the new features added to the open beta NVIDIA Merlin…
]]>Recently, NVIDIA CEO Jensen Huang announced updates to the open beta of NVIDIA Merlin, an end-to-end framework that democratizes the development of large-scale deep learning recommenders. With NVIDIA Merlin, data scientists, machine learning engineers, and researchers can accelerate their entire workflow pipeline from ingesting and training to deploying GPU-accelerated recommenders (Figure 1).
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