A collection of 53 posts tagged with "AIStor"
In the rapidly-evolving world of artificial intelligence (AI) and machine learning (ML), speed and scalability are paramount. The ability to process massive amounts of data in real-time is a critical requirement for organizations looking to leverage AI/ML for competitive advantage. Whether it's training large machine learning models, running complex inference tasks, or scaling data pipelines, the performance
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In this post we look at how search, and specifically OpenSearch can help us identify patterns or see trends in our ever growing data.
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Lately, there has been a trend in the industry to bring data “closer” to home. The result is that organizations now want to keep their data on servers that they own, in their own datacenter or at a colocation provider.
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AWS recently unveiled Amazon S3 Tables, claiming to optimize Iceberg analytics on S3. Yet, these "special buckets" mainly fix AWS's own limits—like request caps—not universal object storage issues. With AIStor, you get unmatched performance, no vendor lock-in, and no extra costs for table maintance.
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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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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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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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Enhance your AI workflows by combining MinIO’s scalable AIStor with Polars, a lightning-fast DataFrame library. Learn how this powerful duo accelerates data pipelines, handles massive datasets, and offers powerful performance and scale.
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Dell generally focused on the filer game, but they dabble in object storage and have a very old offering, ECS. That makes sense, it was a step up from tape and wasn’t suited for dynamic workloads like HDFS modernization or database workloads. Needless to say, AI was out of the question. For a few years now, Dell has been
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Simplifying your data streaming architecture with WarpStream, a cloud-native, Kafka-compatible platform that cuts costs and complexity, recently acquired by Confluent. Paired with MinIO's high-performance object storage, it's a powerful alternative to Kafka for scalable, cost-effective streaming.
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Confluent's WarpStream acquisition highlights the future of data streaming built on object storage. WarpStream’s cloud-native design cuts costs by 85% over traditional Kafka. Believe the hype: object storage drives low-cost, scalable performance.
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MinIO introduced its conditional write feature long before AWS S3’s recent announcement. This powerful tool offers greater control in high-concurrency environments, ensuring data consistency and reliability, especially in AI and ML workflows.
Read more![Gone [to Prod] in 60 Seconds](/content/images/size/w600/2024/09/gonetoprod-in60seconds-noeyes.jpg)
In this post we’ll show you how quickly you can get a production grade of MinIO cluster up and running in just a few seconds. Not only that, but we’ll also show you how you can expand that cluster quickly in just a few seconds as well.
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We often talk about how good, fast and reliable access to data is paramount if you want to have an upper hand in your AI/ML game. Why is this the case? This is because hardware failures happen at different levels.
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