At HPE Discover 2022, The Edge-to-Cloud-Conference, you'll find the best of edge, cloud, and everything in between – all in one place. The good news is that once again you can attend the event LIVE in Las Vegas, June 28-30, 2022. Or you can choose to participate virtually. From the latest insights in secure connectivity and hybrid cloud to AI and unified data analytics, HPE Discover 2022 is the best place to find the information you need to stay ahead of the trends and technologies that can rapidly move your business forward. We invite you to explore the full line-up of sessions on our content catalog.

Sycomp introduces the first IBM Spectrum Scale solution on Azure. As of October 23, Sycomp Storage Fueled by IBM Spectrum Scale, a new cloud storage solution, is now available to the general public on the Microsoft Azure Marketplace. Sycomp Storage Fueled by IBM Spectrum Scale enables organizations to utilize different storage tiers, whether they incorporate the on-prem IBM Spectrum Scale with Azure or deploy a cloud-only solution. What usually would take days, Sycomp offers a new way for organizations to use Azure as a burst platform within a few minutes to a few hours for modern workloads such as High-Performance Computing (HPC), Artificial Intelligence, and Machine Learning (AI/ML). "Sycomp Storage Fueled by IBM Spectrum Scale is now globally available on the Azure Marketplace. Our fantastic HPC, AI/ML, and Storage team has delivered another solution for our clients that provides a cost-effective, automated deployment, and storage tiering platform for their challenging workloads," says Allen Shahdadi, VP of Global Sales, Sycomp.

There is no doubt that the AI marketplace is an attractive, fast-growing sector for IBM Business Partners to get engaged. IDC has forecasted the 2020 AI revenue opportunity at USD 49 billion, growing to USD 98 billion by 2023¹. Customers tell us that infrastructure for AI is not getting better, it’s getting worse. In their struggle to help meet escalating demands, many vendors resort to cobbling disparate portfolios that are not tightly integrated. The result is a jigsaw puzzle of data silos, multiple data copies and independent components, which lead to growing complexity with increased cost of ownership and poor data insights. Data locked in silos is complex to manage—it can also increase operating expenses, which, in turn, drives up capital expenses. It is also hard for users to collect, organize and analyze to extract value. AI needs data to achieve the holistic view that is required for meaningful insights that fuels business growth. How can Business…

Data is the one area where every company has an equal opportunity to be great. Knowing your own data can give your company a clear competitive advantage.[1] Yet, according to a survey conducted by Forrester with global IT, data, and line-of-business decision makers, more than half of the respondents admitted they simply don't know what their AI data needs are[2]. If business leaders do not fully understand their data needs, they can t be expected to understand their infrastructure needs. Mike Leone, Senior Analyst at Enterprise Strategy Group covering Data Platforms, Analytics, and AI, states: "Artificial intelligence is poised to revolutionize business around the globe. While it's easy to get lost in the eye-opening use cases where AI is being applied today, it all starts with a foundational infrastructure that can satisfy the extensive list of AI requirements…" There is no AI without information architecture (IA).

Artificial intelligence (AI) is playing an increasingly critical role in business. By 2020, 30 percent of organizations that fail to apply AI will not be operationally and economically viable, according to one report[1]. And in a survey, 91 percent of infrastructure and operations leaders cite "data" as a main inhibitor of AI initiatives[2]. What does a data professional need to know about AI and its data requirements in order to support his or her organization's AI efforts? Many factors have converged in recent years to make AI viable, including the growth of processing power and advances in AI techniques, notably in the area of deep learning (DL).

Data is the core of artificial intelligence; without data there is no learning and the more data available to the training systems the better the accuracy. Customers have been utilizing high-performance data storage solutions that were originally built for HPC environments to address the data challenges of machine learning. One of the more prominent high-performance storage solutions is IBM's Spectrum Scale file system. In this blog post I outline five specific shortfalls of Spectrum Scale that limit its ability to meet the demands of AI systems. IBM Spectrum Scale (aka IBM GPFS) was developed 25 years ago to support the high throughput needs of multimedia applications.

In the first part of this "future of IT" series, I posed the question: What will IT look like a decade from now? To begin the conversation, I outlined the three major segments of IT -- procedural IT, statistical IT and machine learning IT -- noting that machine learning will see the most growth in the next ten years. Breakthroughs in machine learning are already finding applications in numerous industries, and software houses are increasingly staffed with data scientists for that purpose. But there's more to pay attention to in the future of IT. Neural processing demands a huge amount of parallelism but relatively low performance for each of the parallel "threads" in the execution phase.