NVIDIA Merlin for Recommender Systems
NVIDIA Merlin™ is an end-to-end framework for building high-performing recommenders at any scale. Using NVIDIA Merlin, data scientists and machine learning engineers are empowered to streamline building pipelines for session-based recommendations and more. Merlin components and capabilities are optimized to support the retrieval, filtering, scoring, and ordering of hundreds of terabytes of data, all accessible through easy-to-use APIs. With Merlin, better predictions, increased click-through rates, and faster deployment to production are within reach.
Building personalized customer engagement, retention, and brand loyalty strengthens companies’ economic engines. Companies that rely heavily upon engaging online with potential and established customers to drive customer retention and upsell are considering session-based recommender methods. Session-based recommenders enable data scientists, machine learning engineers, and their companies to build a streamlined recommender pipeline when little or no online user history is available. Leading companies are using session-based recommenders to increase model accuracy and drive quality customer engagement. The NVIDIA Merlin AI workflow for next- item prediction is designed to help companies build effective, personalized recommendations.
Optimal Inference for Generative AI Workloads
NVIDIA NIM, part of NVIDIA AI Enterprise, is a set of easy-to-use inference microservices designed to accelerate the deployment of generative AI across your enterprise. This versatile runtime supports open community models and NVIDIA AI Foundation models from the NVIDIA API catalog, as well as custom AI models. NIM builds on NVIDIA Triton™ Inference Server, a powerful and scalable open source platform for deploying AI models, and is optimized for large language model (LLM) inference on NVIDIA GPUs with NVIDIA TensorRT-LLM. NIM is engineered to facilitate seamless AI inferencing with the high throughput and low latency, while preserving the accuracy of predictions. You can now deploy AI applications anywhere with confidence, whether on-premises or in the cloud.
NVIDIA RAPIDS for GPU-Accelerated Data Processing
Retailers are realizing significant ROI and cost savings using data analytics and machine learning.
Many enterprises use Apache Spark for key operations such as ingesting raw data into data lakes, business process analytics, loading data into data warehouses, and data preprocessing at the start of machine learning pipelines. However, slow, CPU-based infrastructure is constraining growing workloads. And slow processing costs time, money, and energy — resulting in a larger carbon footprint.
The NVIDIA RAPIDS™ Accelerator for Apache Spark takes advantage of NVIDIA GPUs to accelerate Apache Spark workloads without code changes. It operates as a plug-in to popular Apache Spark platforms. The RAPIDS Accelerator speeds up selected Spark operations while allowing other operations to continue running on the CPU. As a result, processing time is accelerated up to 5X, allowing the same work to be completed with 4X less infrastructure.