A collection of 119 posts tagged with "AI/ML"
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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One of the biggest challenges facing organizations today for AI and data management is access to reliable infrastructure and compute resources. The Intel Tiber Developer Cloud is purpose-built for engineers who need an environment for proof-of-concepts, experimentation, model training, and service deployments. Unlike other clouds, which can be unapproachable and complex, the Intel Tiber Developer Cloud is simple and easy
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AIStor is a foundational component for creating and executing complex data workflows. At the core of this event-driven functionality is MinIO bucket notifications using Kafka.
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In this post we’ll show you how you visualize the cluster metrics in a web browser and also we’ll set up alerting so that when something like a drive needs to be replaced or drive runs out of space we can get alerted for it.
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To ensure AI success, start by hiring a data engineer, not an AI/ML expert. Learn from our experience and find out why a strong data foundation—focused on object storage, data lakehouses, and optimized pipelines—is critical for scalable, efficient AI/ML workloads.
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You’ve surely version controlled code in the past. But have you version controlled your data? Did you ever want to collaborate on large sets of data with various teams without committing a large chunk?
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This post first appeared on The New Stack on July 29th, 2024. Artificial Intelligence is in the middle of a perfect storm in the software industry, and now Mark Zuckerberg is calling for open-sourced AI. Three powerful perspectives are colliding on how to control AI: 1. All AI should be open-source for sharing and transparency. 2. Keep AI closed-source and
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In this post we explain how to use Splunk's advanced log analytics to help understand the performance of AIStor and the data under management.
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The modern enterprise defines itself by its data. This requires a data infrastructure for AI/ML as well as a data infrastructure that is the foundation for a Modern Datalake capable of supporting business intelligence, data analytics, and data science. This is true if they are behind, getting started or using AI for advanced insights. For the foreseeable future, this
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The team at Insight Partners just released their State of Enterprise Tech report for 2024. There is a lot to consume in the 60+ slides, but we cherry picked the things that should be interesting to our audience - and frankly there is a lot of interesting stuff. I will leave the survey methodology stuff for you to consume, but
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An embedding subsystem is one of four subsystems needed to implement Retrieval Augmented Generation. It turns your custom corpus into a database of vectors that can be searched for semantic meaning. The other subsystems are the data pipeline for creating your custom corpus, the retriever for querying the vector database to add more context to a user query, and finally,
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One of the reasons that MinIO is so performant is that we do the granular work that others will not or cannot. From SIMD acceleration to the AVX-512 optimizations we have done the hard stuff. Recent developments for the ARM CPU architecture, in particular Scalable Vector Extensions (SVE), presented us with the opportunity to deliver significant performance and efficiency gains
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With all the talk in the industry today regarding large language models with their encoders, decoders, multi-headed attention layers, and billions (soon trillions) of parameters, it is tempting to believe that good AI is the result of model design only. Unfortunately, this is not the case. Good AI requires more than a well-designed model. It also requires properly constructed training
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MLOps, short for Machine Learning Operations, is a set of practices and tools aimed at addressing the specific needs of engineers building models and moving them into production. Some organizations start off with a few homegrown tools that version datasets after each experiment and checkpoint models after every epoch of training. On the other hand, many organizations have chosen to
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Migrate from Hitachi Content Platform (HCP) to MinIO using the HCP-to-MinIO tool. Migration is a no-brainer given how MinIO offers modern, scalable, high-performance storage optimized for AI.
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In this blog, we will demonstrate how to use MinIO to build a Retrieval Augmented Generation(RAG) based chat application using commodity hardware.
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tl;dr: In this post, we will explore four technical reasons why AI workloads rely on high performance object store. 1. No Limits on Unstructured Data In the current paradigm of machine learning, performance and ability scales with compute, which is really a proxy for dataset size and model size (Scaling Laws for Neural Language Models, Kaplan et. al.). Over
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