NVIDIA and Cloudera continue to advance AI technologies
Cloudera is continuing to support advanced NVIDIA AI technologies to offer the best-in-class applications for enterprise digital transformation.
Public and private cloud environments are to be enabled in the company’s support for key NVIDIA technologies, having been designed to help enable customers to efficiently build and deploy AI applications. Using NVIDIA GPUs, this new phase in Cloudera’s technology collaboration with NVIDIA will add multi-generational GPU capabilities for data engineering, machine learning and AI.
The partnership expansion comes in the midst of NVIDIA being impacted by the US AI chip restrictions on China. The country accounts for 25% of the company’s chip revenues.
Working to accelerate AI and machine learning workloads
Cloudera Machine Learning (CML) is a leading service of the Cloudera Data Platform that seeks to empower enterprises to create their own AI applications. By utilising their own proprietary data assets to create secure and accurate responses, the company helps customers unlock the potential of open-source large language models (LLMs).
Speaking on Data Platform, Cloudera’s Anthony Behan told AI Magazine: “The Cloudera Data Platform is there to help customers drive innovation” and that it offers a “selection of purpose-built data services allowing expansion across the data lifecycle, from the edge to AI, whether that’s streaming massive data volumes, or deploying and monitoring next generation AI and ML models.”
Cloudera’s CML service is now turning its attention to support the NVIDIA H100 GPU in public clouds and in data centres. This next-generation acceleration aims to empower Cloudera's data platform, enable faster insights and more efficient generative AI workloads. As a result, the ability to fine-tune models on larger datasets and to host larger models in production could emerge.
The enterprise-grade security and governance of CML means that businesses can better leverage the power of NVIDIA GPUs without compromising on data security.
NVIDIA partnerships continue to enhance global AI operations
Another key benefit is users gaining an enhanced ability to accelerate data pipelines with GPUs in Cloudera’s private cloud. WIth Cloudera Data Engineering, users can build production-ready data pipelines from various sources.
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 compared to standard CPUs, allowing NVIDIA customers to better take advantage of GPUs in upstream data processing pipelines.
As a result, the hope is that utilisation of these GPUs is increased and can offer a higher return on investment.
“GPU acceleration applies to all phases of the AI application lifecycle - from data pipelines for ingestion and curation, data preparation, model development and tuning, to inference and model serving,” says Priyank Patel, Vice President of Product Management at Cloudera.
“NVIDIA's leadership in AI computing perfectly complements Cloudera's leadership in data management, offering customers a complete solution to harness the power of GPUs across the entire AI lifecycle.”
NVIDIA is committed to scaling the data centre to help enterprises stay ahead in generative AI developments. This week, it also announced a partnership with Dell Technologies to serve as a ‘blueprint’ for the next generation of large-scale AI clusters.
Dell CEO Michael Dell says that their new designs will “help meet the demands of LLMs and GenAI applications with one of the fastest AI systems in the world” and feature “2,048 NVIDIA H100 Tensor Core GPUs, 256 Dell PowerEdge XE9680 AI servers, and a robust Spectrum-X Ethernet AI network.”
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