Cloud lakehouses break the bank at scale and compromise control. On-prem Iceberg lakehouses deliver speed, savings, and sovereignty. From cancer research to finance, real-world deployments prove it: petabyte-scale performance, full control, and lower TCO are within reach.
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In the previous blog of this series, we discussed the basic user-level functions of the Model Context Protocol (MCP) server for MinIO AIStor. We learned how to review a bucket’s contents, analyze objects, and tag them for future processing using human-language commands and simply chatting with the cluster via an LLM such as Anthropic Claude. In this blog, we’
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GenAI is entering the agentic phase, with software agents collaborating with humans and other agents to reason and achieve complex goals. Agents are already demonstrating incredible intelligence and are very helpful with question answering, but as with humans, they need the ability to discover and access software applications and other services to actually perform useful work. The creators of such
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Want real-time analytics and blazing-fast performance? Learn how to build a high-speed, on-prem pipeline with Materialize and MinIO AIStor—faster than S3, high thoughput, and built for AI. Includes a full tutorial to get you up and running locally.
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Tl;dr: We train a small LLM to become good at reasoning with reinforcement learning (similar to the process that led to Deepseek R1) all against AIStor AIHub, an on-premises model repository. Based on the great GRPO demo by will brown. Motivation: A growing requirement for teams is the need for an organized, secure, "single source of truth"
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In today’s AI-driven enterprise landscape, resource optimization has evolved from a desirable goal into an operational imperative. As organizations scale their artificial intelligence initiatives to meet rising demands for innovation, the efficient orchestration of compute resources directly shapes operational performance and model precision. The forthcoming integration of NVIDIA GPUDirect Storage (GDS) with MinIO AIStor is a co-engineered solution slated
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The Arm architecture is revolutionizing the hyperscale cloud, propelled by its Total Cost of Ownership (TCO) advantages—lower power consumption and reduced cooling requirements—that enable sustainable, high-performance computing at scale. Industry leaders like AWS, Azure, and GCP are embracing Arm to drive their latest compute instances for AI training, harnessing its efficiency to meet the demands of data-intensive workloads.
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Building upon AIStor's robust AI capabilities, MinIO's PromptObject has been enabling users to interact with their data through natural language queries as described here. PromptObject transforms how users interact with stored objects by allowing them to ask questions about their data's content and extract information using natural language—eliminating the need to write complex
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Modern enterprises seeking to leverage AI capabilities often face a significant hurdle: the complex deployment and management of GPU infrastructure in their Kubernetes environments. MinIO's AIStor addresses this challenge head-on by integrating the NVIDIA GPU Operator, revolutionizing how organizations deploy and manage GPU resources for AI workloads. Through automated GPU setup, driver management, and resource optimization, this integration
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MinLZ is a compression algorithm developed by MinIO. The main goal is to provide a format that offers the best-in-class compression while providing very fast decompression even with modest hardware.
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MLflow Model Registry allows you to manage models that are destined for a production environment. This post picks up where my last post on MLflow Tracking left off. In my Tracking post I showed how to log parameters, metrics, artifacts, and models. If you have not read it, then give it a read when you get a chance. In this
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A cybersecurity firm faced soaring cloud costs and performance bottlenecks with AWS S3 as their log data grew to a multi-exabyte scale. They adopted MinIO AIStor for high-performance, S3-compatible object storage, cutting costs and boosting efficiency.
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Apache XTable enables interoperability between Iceberg, Hudi, and Delta Lake by translating metadata, eliminating the need for data migration. This makes it easier to adopt new query engines, optimize performance, and build a more flexible data architecture.
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In several previous posts on MLOps tooling, I showed how many popular MLOps tools track metrics associated with model training experiments. I also showed how they use MinIO to store the unstructured data that is a part of the model training pipeline. However, a good MLOps tool should do more than manage your experiments, datasets, and models. It should be
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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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Choosing the right open table format—Apache Iceberg, Delta Lake, or Apache Hudi—can make or break your data lakehouse. This guide breaks down their strengths, how they integrate with object storage, and which one is best for AI, analytics, and real-time workloads.
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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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This post first appeared on The New Stack on January 16th, 2025. Often, while accessing the legitimacy of a new technology receiving a lot of hype, studying existing core capabilities and history is helpful. If the new technology in question is not based on existing or imminent capabilities, we can label it as “hype” and move on. Another litmus test
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2025 has inherited a slew of geopolitical concerns that started years ago. U.S. Foreign policy, U.S. - China Relations, China’s geopolitical maneuvers, Conflicts in the Middle East, Russian Ukraine war, and cybersecurity threats. Additionally, new leadership in the United States adds to the uncertainty created by these concerns. And, as if all this were not enough, the
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dbt’s acquisition of SDF Labs reinforces a powerful trend: the modern data stack is open. Learn why this matters for performance, interoperability, and future-proofing your data strategy.
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