WekaIO's President sees the company as the Tesla of storage suppliers, and says OEM Hitachi Vantara is making inroads into the Dell EMC Isilon customer base as Weka crosses the chasm between it and general enterprise use. WekaIO's scalable, parallel and high-performance filesystem software has made its name in high-performance computing and become popular in enterprises that have HPC use cases -- such as AI, machine learning, and genomics. It's now set to cross over into more general enterprise file workloads. BMW motorcycle-riding Jonathan Martin became WekaIO's President this month. He had previously been the Chief Marketing Officer at Hitachi Vantara, serving from March 2019 to May 2021.

Digital disruption has broken out of Silicon Valley. Any company, no matter how nuts-and-bolts, can be disrupted by a digital competitor; equally, any company could be that digital disruptor. The discussion was kick-started by two leading industry thinkers: Andrew Moore, chief transformation officer of chipmaking giant Intel, and Nigel Moulton, chief technology officer at Dell EMC, part of a corporation that services 99% of the Fortune 500 companies. Their remarks sparked lively discussion. Both Intel's Mr Moore and Dell EMC's Mr Moulton spend a lot of time talking to leading companies about their digital transformation journey, and they kicked off with a tough message: it's hard work.

Digital disruption has broken out of Silicon Valley. Any company, no matter how nuts-and-bolts, can be disrupted by a digital competitor; equally, any company could be that digital disruptor. The discussion was kick-started by two leading industry thinkers: Andrew Moore, chief transformation officer of chipmaking giant Intel, and Nigel Moulton, chief technology officer at Dell EMC, part of a corporation that services 99% of the Fortune 500 companies. Their remarks sparked lively discussion. Both Intel's Mr Moore and Dell EMC's Mr Moulton spend a lot of time talking to leading companies about their digital transformation journey, and they kicked off with a tough message: it's hard work.

As we've talked about in the past, the focus on data – how much is being generated, where it's being created, the tools needed to take advantage of it, the shortage of skilled talent to manage it, and so on – is rapidly changing the way enterprises are operating both in the datacenter and in the cloud and dictating many of the product roadmaps being developed by tech vendors. Automation, analytics, artificial intelligence (AI) and machine learning, and the ability to easily move applications and data between on-premises and cloud environments are the focus of much of what OEMs and other tech players are doing. And all of this is being accelerated by the COVID-19 pandemic, which is speeding up enterprise movement to the cloud and forcing them to adapt to a suddenly widely distributed workforce, trends that won't be changing any time soon as the coronavirus outbreak tightens its grip, particularly in the United States. OEMs over the past several months have been particularly aggressive in expanding their offerings in the storage sector, which is playing a central role in help enterprises bridge the space between the datacenter, the cloud and the network edge and to deal with the vast amounts of structured and – in particular – unstructured data being created. That can be seen in announcements that some of the larger vendors have made over the past few months.

New York, New York--(Newsfile Corp. - March 31, 2020) - Dell EMC, a leading provider of full-stack solutions for data science teams, and Comet, the industry-leading meta machine learning experimentation platform, announced a collaboration with a reference architecture for data science teams looking to harness the power of the Dell EMC infrastructure in tandem with Comet's meta machine learning platform. With Dell EMC PowerEdge reference architectures, organizations can deploy artificial intelligence workload-optimized rack systems approximately 6-12 months faster than it would have taken to design the correct configurations and deploy the solution. Organizations can now rely on architectures that are tested and validated by our Dell engineers and know that services are available when and where you need them. "Orchestrating and managing the stack for enterprise data science teams is a huge pain point for many of our customers," said Gideon Mendels, Co-founder/CEO, Comet. "Dell EMC's Kubeflow and Kubernetes solutions are best-in-class and an excellent choice for any data science team looking to build a robust and scalable ML platform."

"There's a lot of value in the data that organizations collect, and HPC and AI are helping organizations get the most out of this data," said Thierry Pellegrino, vice president of HPC at Dell Technologies. "We're committed to building solutions that simplify the use and deployment of these technologies for organizations of all sizes and at all stages of deployment." Dell is expanding its portfolio of Dell EMC Ready Solutions for HPC Storage with new, turnkey solutions for ThinkParQ's BeeGFS and ArcaStream's PixStor file systems. Offering a combination of technology partners' software with Dell EMC hardware, networking and support, based on engineered and tested designs, Dell EMC Ready Solutions for HPC Storage simplify and speed deployment and solutions management. Dell EMC Ready Solutions for HPC BeeGFS Storage, with ThinkParQ's software-defined parallel file system, speeds-up input/output (I/O)-intensive workloads with the ability to scale from small clusters to enterprise-class systems on premises or in the cloud.

This introduces a new high-performance, high capacity, reduced cost inference choice for data centers and machine learning service providers. It is the purpose-designed, open PCIe architecture of the DSS 8440 that enables us to readily expand accelerator options for our customers as the market demands. This latest addition to our powerhouse machine learning server is further proof of Dell EMC's commitment to supporting our customers as they compete in the rapidly emerging AI arena. The DSS 8440 is a 4U 2-socket accelerator-optimized server designed to deliver exceptionally high compute performance for both training and inference. Its open architecture, based on a high performance switched PCIe fabric, maximizes customer choice for machine learning infrastructure while also delivering best-of-breed technology.

In the first article in this series, we discussed how humans have always desired to better understand the present and predict the future.1 The algorithms to help achieve this understanding have been around for decades, including even those of artificial intelligence (AI) approaches for enabling computers to reason about things that normally require human intelligence. However, only in recent years have we accumulated the massive digital data and developed the sufficiently powerful processors needed to put these AI algorithms to work on real human and business problems, with excellent performance and accuracy, on a broad scale. In this article, we describe some of the fundamental technologies and processes that enable enterprises to put AI to work to transform their businesses. In particular, we explain the concepts of data analytics, data science and machine learning, including deep learning.

Adoption of deep learning has gained a lot of traction during the last two or three years across a wide variety of use cases and has become a top area of interest for many enterprises around the world. Yet, these enterprises still need to determine how best to spend their investment to yield meaningful results for their business. Once an organization has identified the most advantageous use case with which to begin, it is important for CIOs to consider a thoroughly deliberated architectural design. On the one hand, the toolchain for deep learning environments is diverse. There are a wide variety of development toolkits, frameworks and libraries from which to choose. Even the choice of hardware to run the deep learning workload can have a significant impact on an organization's results.

At Supercomputing 2019, Dell Technologies introduced several new solutions, reference architectures and portfolio advancements all designed to simplify and accelerate customers' high performance computing (HPC) and artificial intelligence (AI) efforts. Continued adoption of AI to solve real-world problems has spurred growth across the HPC industry. According to a recent report from Hyperion Research, the global HPC industry is expected to grow by 7,1% to more than $39,2-billion by 2023, and HPC-server based AI is expected to rise by more than 29% from 2018 to 2023, reaching $2,7-billion in 2023. "There's a lot of value in the data that organizations collect, and HPC and AI are helping organizations get the most out of this data," says Thierry Pellegrino, vice-president of HPC at Dell Technologies. "We're committed to building solutions that simplify the use and deployment of these technologies for organisations of all sizes and at all stages of deployment."