AIStor's Prompt API transforms healthcare data—analyze MRI scans, uncover trends in medical records, and accelerate research with natural language prompts. From automating image analysis to streamlining patient care, it empowers better outcomes for providers, researchers, and patients
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We’ll dive deep into the network configurations with which you can configure AIStor to the different replication strategies and network topologies that can be used to ensure your data is stored and accessed efficiently across multiple AIStor deployments.
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How does Exness handle massive data volumes and demanding AI/ML workloads? By moving to an on-prem infrastructure powered by MinIO. From scaling their data lake to managing traffic peaks of 200 Gbps, MinIO supports their AI workflows, disaster recovery, and more.
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Your DevOps Engineer’s customer should be your AI/ML Engineering Team. The DevOps Engineer is there to ease the friction points in infrastructure so AI/ML folks can focus on the task at hand. Any issues that come with the infrastructure should be the responsibility of the DevOps Engineer.
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Almost a year ago (actually 11 months ago), I wrote about the “Starving GPU Problem” and how the horsepower of Nvidia’s Graphic Processing Units (GPUs) could be so powerful that your network and your storage solution may not be able to keep up - preventing your expensive GPUs from being fully utilized. Well, in those short 11 months, a
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In this tutorial, we’ll bake the MinIO binary and the service dependencies, such as the username and group required for MinIO. We are baking this instead of frying because these will remain the same no matter how we configure and launch our image.
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A mobile application is a company's brand available on demand. It is a window into any service or product an organization offers. At Kobiton, they understand this—it is their mission to improve mobile applications through testing. Kobiton is a mobile testing platform that allows customers to perform manual and automated testing on real mobile devices from anywhere
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Have you ever wondered how the big dogs with hundreds of apps and millions of users manage their Continuous Integration (Builds) and Continuous Delivery (Deployments) workflows?
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An educational services leader transformed its IT with MinIO, achieving 40% faster performance and 30% lower costs. Powered by Kubernetes, MinIO supports AI, ML, and scalable data lakes with active-active replication. Discover how they modernized for innovation and resilience.
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Tl;dr: GET, PUT, PROMPT. It’s now possible to summarize, talk with, and ask questions about an object that is stored on MinIO with just natural language using the new PromptObject API. In this post, we explore a few use-cases of this new API along with code examples. Motivation: Object storage and the S3 API’s ubiquity can be
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Today we announced the launch of AIStor, a new release which represents our singular focus on building the world’s finest object store for AI/ML workloads. The AIStor represents a year of accelerated learning from our biggest customers. MinIO is operating at a different level than the rest of the industry. We have multiple clients with more than 100
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The object storage world to date has been defined by the S3 API concepts of PUT and GET. The world in which we live now, however, requires more. Given that MinIO has more S3 deployments than even Amazon, it fell to us to come up with the next great S3 API. That new API is the Prompt API and it
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As the demands of AI and machine learning continue to accelerate, data center networking is evolving rapidly to keep pace. For many enterprises, 400GbE and even 800GbE are becoming standard choices, driven by the need for high-speed, low-latency data transfer for AI workloads that are both data-intensive and time-sensitive. AI models for tasks like large language processing, real-time analytics, and
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One of the newest features of the AIStor is a private cloud version of the highly popular, open-source project, Hugging Face. This post details how AIStor’s AIHub effectively creates an API compatible, private cloud version of Hugging Face that is fully under the enterprise's control. Before we get started, it makes sense to introduce Hugging Face. Hugging
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The MinIO Console has been an evolving product for several years now. Every time we learn, we think about how to improve this incredibly important part of our interaction framework. First came the Console, which saw massive adoption within a year of its introduction. More than 10K organizations to be more specific.Next came the enterprise Console. That moved from
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As AI workloads drive cloud costs through the roof, many companies are rethinking their approach. Moving select AI tasks back on-prem offers a path to predictable costs, improved performance, and stronger data control.
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Interoperability is the key to building a flexible, future-ready AI data stack. As proprietary systems lock down innovation and drive up costs, open tools like S3-compatible storage and multi-format table systems offer the freedom to scale and adapt.
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MinIO recently surveyed 656 IT leaders as part of a primary research initiative with User Evidence. The results were very interesting and underscore the massive sea change we are seeing in the enterprise, both around the movement to object storage and the interest in using object storage as the primary building block for an organization’s AI initiatives. We will
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AIStor and Metaflow can be deployed anywhere on any type of infrastructure. In fact more often than not we have folks deploying AIStor and metaflow alongside each other as part of the deployment pipeline.
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Before diving into Amazon’s S3 Connector for PyTorch, it is worthwhile to introduce the problem it is intended to solve. Many AI models need to be trained on data that cannot fit into memory. Furthermore, many really interesting models being built for computer vision and generative AI use data that cannot even fit on the disk drive that comes
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