Many people involved in artificial intelligence don’t spend much time considering storage infrastructure. That’s the topic of this episode of Utilizing AI, which features Ace Stryker of Solidigm discussing the role of flash storage in AI infrastructure with Frederic Van Haren and Stephen Foskett. Considering the cost of GPUs and the rest of the AI stack, idle time is the enemy. That’s why it’s critical to have a low-latency storage layer to support tasks like training.

AI training is a uniquely data-hungry application, and it requires a special data pipeline to keep expensive GPUs fed. This episode of Utilizing Tech focuses on the data platform for machine learning, featuring Molly Presley of Hammerspace along with Frederic Van Haren and Stephen Foskett. Nothing is worse than idle hardware, especially when it comes to expensive GPUs intended for ML training. Performance is important, but parallel access and access to multiple systems is just as important. Building an AI training environment requires identifying and eliminating bottlenecks at every layer, but many systems are simply not capable of scaling to the extent required by the largest GPU clusters. But a data pipeline goes way beyond storage: Training requires checkpoints, metadata, and access to different data points. And different models have unique requirements as well. Ultimately, AI applications require a flexible data pipeline not just high-performance storage.

Most people envision AI as a cool and orderly datacenter activity, but this technology will soon be everywhere. This episode of the On-Premise IT podcast contrasts the AI-based greenhouses of Nature Fresh Farms, as presented by guest Keith Bradley at AI Field Day, with the massive GPU-bound infrastructure many people imagine. Allyson Klein, Frederic Van Haren, and Stephen Foskett attended AI Field Day and were intrigued by the ways AI can process data from cameras and other sensors in a greenhouse environment.

AI is powering breakthroughs across all domains. In this episode of Utilizing AI Podcast brought to you by Tech Field Day, part of The Futurum Group, David Kanter, Founder and Broad Member of MLCommons, joins hosts, Stephen Foskett and Frederic Van Haren, to talk about MLCommons’ role in driving valuable AI solutions, and helping organizations overcome the challenges around AI safety. MLCommons’ set of benchmarks provides transparent ratings and reviews of a wide range of products guiding buyers towards better purchase decisions.

In a series of bold moves, Broadcom has restructured VMware shifting to subscription-only licenses across all offerings. Broadcom CEO Hock Tan wants VMware to focus on resiliency, developer efficiency, and infrastructure efficiency. Tan outlines three key strategies: radical simplification, substantial R&D investment for on-premises to private cloud solutions, and enhancing the ecosystem. The discontinuation of products, including Aria SaaS and Carbon Black, and divesture of EUC is part of a plan to streamline VMware’s portfolio into bundled suites. Reactions are decidedly mixed, but we are starting to see the strategy come together.

AI is all about data, so it is no surprise that enterprises are deploying their own private AI inside the firewall. This episode of Utilizing Tech brings Chris Wolf, Global Head of AI and Advanced Services at VMware by Broadcom, to discuss private AI with Frederic Van Haren and Stephen Foskett. Companies looking to deploy AI are finding that it doesn’t require nearly as much hardware as expected, software is widely available, and are reluctant to trust sensitive data to a service provider. VMware by Broadcom is deploying their own private AI code assist, keeping proprietary software and standards inside the firewall. But the solution also helps the AI team be more agile and responsive to the needs of the business and customers. One of the first use cases they found for private AI was customer support, and this is tightly integrated with internal documentation and sources to ensure valid responses. The biggest challenge is to integrate unstructured data, which can be spread across many locations, and this is actively being investigated by companies like Broadcom as well as projects like LlamaIndex. VMware has contributed back to the open source community, notably with the Ray workload scheduler, open source models, and related projects. It’s important to build community and long-term engagement and support for open source as well, and this is in keeping with the overall trends in the AI community. Organizations looking to get started with private AI should consider the VMware by Broadcom reference architecture, which incorporates best practices at a smaller scale and pick a use case that provides immediate value.

Generative AI is becoming a key tool for software developers, and businesses are embracing it as well. This episode of the On-Premise IT podcast brings Paul Nashawaty of The Futurum Group, data expert Karen Lopez, and Stephen Foskett together to discuss how AI is impacting application development. Generative AI is incredibly compelling, rapidly producing credible output. that it’s hard to put a stop to it. Rather than trying to stand in the way, companies are looking for better quality tools, with data privacy and compliance capabilities to fend off the negatives that can arise from AI-generated content. AI can also help with tasks like documentation and testing that are less popular and more problematic, and these can improve overall code quality as well.

Data is the foundation on which AI models are built, and integration of enterprise data will be the key to generative AI applications. This episode of Utilizing Tech brings Nick Magnuson and Clive Bearman from Qlik to discuss the integration of data and AI with Frederic Van Haren and Stephen Foskett. Enterprises sometimes worry that their data will never be ready for AI or that they will feed models with too much low-quality data, and overcoming this issue is one of the first hurdles. Another application for machine learning is improving data quality, organizing and tagging unstructured data for applications. The concept of curated data is an interesting one, since it promises to elevate the value of enterprise data. But what if a flood of data causes the model to make the wrong connections? If data is to be a product it must be profiled, tagged, and organized, and ML can help make this happen. The trend of generative AI is driving budgets and priorities to make data more useful and organized, but even unstructured data streams can be valuable. The application of large language models to structured data is promising as well, since it enables people to query these data sets even if they lack the background and skills to construct queries.

Everybody says that 2024 is the year of AI, but our industry has been building the foundation for years. From processors and accelerators to storage and data platforms, applications that leverage machine learning have special demands. That’s the focus of AI Field Day, which features an entire day sponsored by Intel, plus two more days filled with presentations by a diverse range of companies. The next AI Field Day event is February 21st through 23rd. The event will be broadcast live on LinkedIn and on the Tech Field Day website, starting at 8:00 a.m. Pacific time, Wednesday through Friday, with more presentations throughout the day from VMware by Broadcom, VAST Data, Hammerspace, Qlik, Solidigm, and a whole day of presentations presented by Intel with key partners and customers.

Modern workloads are overloading hardware systems, and the CPUs in the market today aren’t up to the task. In this episode of On-Premise IT Podcast recorded on the premises of the Cloud Field Day event in California, host Stephen Foskett is joined by Thomas LaRock, Shala Warner, and Jim Czuprynski from the IT world, to talk about innovation in hardware. The discussion addresses the burning question of whether investing in more specialized hardware solves the problem. Hear the panel explain how hardware innovation is intertwined with software innovation, and how the two components come together to power cutting-edge workloads.