Cloudera announced additional support for key NVIDIA technologies in public and private clouds to help enable customers to efficiently build and deploy best-in-class applications for artificial intelligence. This new phase in Cloudera's technology collaboration with NVIDIA adds multigenerational GPU capabilities for data engineering, machine learning and artificial intelligence in both public and private clouds: Accelerate AI and Machine Learning Workloads in Cloudera on Public Cloud and On-Premises Using NVIDIA GPUs Cloudera Machine Learning [2] (CML) is a leading service of Cloudera Data Platform that empowers enterprises to create their own AI applications, unlocking the potential of open-source Large Language Models (LLMs) by utilizing their own proprietary data assets to create secure and contextually-accurate responses. CML service now supports the cutting-edge NVIDIA H100 GPU in public clouds and in data centers.

This next-generation acceleration empowers Cloudera's data platform, enabling faster insights and more efficient generative AI workloads. This results in the ability to fine-tune models on larger datasets and to host larger models in production. The enterprise-grade security and governance of CML means businesses can leverage the power of NVIDIA GPUs without compromising on data security.

Cloudera Data Engineering [3] (CDE) is a data service that enables users to build reliable and production-ready data pipelines from sensors, social media, marketing, payment, HR, ERP, CRM or other systems on the open data lakehouse with built-in security and governance, orchestrated with Apache Airflow, an open source project for building pipelines in machine learning. With NVIDIA Spark RAPIDS integration in CDE, extracting, transforming, and loading (ETL) workloads can now be accelerated without the need to refactor. Existing Spark ETL applications can seamlessly be GPU-accelerated by a factor of 7x overall and up to 16x on select queries [4] compared to standard CPUs (based on internal benchmarks).

This allows customers of NVIDIA to take advantage of GPUs in upstream data processing pipelines, increasing utilization of these GPUs and demonstrating higher return on investment.