Announcing General Availability of MinIO AIStor Tables
AIStor Tables: The first data store to build-in Apache Iceberg™ V3, unifying both tables and objects for analytics and AI at scale
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A collection of 124 posts tagged with "AI/ML"
MinIO AIStor-powered Data Lakehouse Helps Accelerate AI and Analytics for a Global Financial Institution
1. Executive Summary A global financial institution modernized its Data Analytics Platform, shifting from legacy, appliance-based storage to a high-performance, cloud-native MinIO AIStor-powered data lakehouse. This transition cut deployment time by 50%, maximized data insights, boosted AI model efficiency, improved existing analytics workflows, and enabled entirely new AI-driven use cases. 2. The Environment Like many global financial institutions, this organization
Read moreIntroducing MinIO ExaPOD: The Reference Architecture for Exascale AI
AI has shifted the baseline for infrastructure, from managing petabytes to operating seamlessly at exabyte scale. Agentic systems, long-context language models, simulation pipelines, and large-scale observability now demand storage architectures purpose-built for this data reality. Today we’re thrilled to announce ExaPOD, MinIO’s validated reference architecture for the exascale era: a one-exabyte, highly dense and efficient building block that
Read moreFrom hours to minutes: Automating cluster deployments with AIStor MCP server
As a MinIO Curriculum Engineer, I spend a lot of time creating demos and learning environments for customers. These environments need to be realistic, comprehensive, and ready to showcase MinIO AIStor's capabilities in real-world scenarios. But here's the thing - setting up a proper demo environment manually can take hours of low-value, repetitive work. Let me
Read moreThe Full Stack AI Engineer Skills Guide: From MLOps to LLMs
Anyone who has worked in a team environment knows that every successful team has one go-to person—that special individual who can help you regardless of the nature of your problem. On a traditional software development team, this individual is an expert programmer and is also an expert in one other technology, which could be a database technology like Snowflake
Read moreOpenAI Open Models: A Gamechanger for Enterprise AI
OpenAI’s move this week to release two new open-weight AI models (gpt-oss-120b and gpt-oss-20b) just changed Enterprise Data Infrastructure forever. The news has rightfully made ripples across the tech ecosystem. Why? * Because these models are released under the Apache 2.0 license, users can for the first time in 5 years, run OpenAI models right on their own devices.
Read moreTurbocharged Storage: MinIO, KIOXIA, and AMD team up to take on AI
MinIO, the leader in high-performance AI storage, has once again raised the bar in the AI infrastructure industry with its groundbreaking MinIO AIStor platform. Leveraging next-generation AMD hardware, KIOXIA NVMe™ SSDs, and cutting-edge software optimizations, MinIO AIStor delivers unmatched performance, scalability, and efficiency for AI-driven and other data intensive workloads. Today, we are excited to share benchmark results that demonstrate
Read moreMLOps Architecture Guide for AI Infrastructure
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
Read moreAI ML Architecture: Modern Datalake Reference Guide
An abbreviated version of this post appeared on The New Stack on March 19th, 2024. In enterprise artificial intelligence, there are two main types of models: discriminative and generative. Discriminative models are used to classify or predict data, while generative models are used to create new data. Even though Generative AI has dominated the news of late, organizations are still
Read moreModel Context Protocol (MCP) Server for AIStor: How it works
In the previous blog posts of this series, we discussed the user-level and admin-level functions of the Model Context Protocol (MCP) server for MinIO AIStor. In the first blog, we learned how to review the bucket’s contents, analyze objects, and tag them for future processing. In the second blog, we also learned how to use admin commands and get
Read moreModel Context Protocol (MCP) Server for AIStor: administration functions
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’
Read moreIntroducing Model Context Protocol (MCP) Server for MinIO AIStor
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
Read moreDeepseek-style Reinforcement Learning Against Object Store
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"
Read moreAIStor Integration with NVIDIA NIM™
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
Read moreEnterprise AI Infrastructure Made Easy with AIStor and NVIDIA GPUs
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
Read moreMLflow Model Registry and MinIO
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
Read moreDeploying Models to Kubernetes with AIStor, MLflow and KServe
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
Read moreUnlocking AI/ML Performance with AMD + MinIO
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
Read moreThe Architect’s Guide to Understanding Agentic AI
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
Read moreMitigating Geopolitical Concerns with a Sovereign Private Cloud
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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