A collection of 117 posts tagged with "AI/ML"
Dell ECS's “Data Movement”, also called copy-to-cloud is a feature introduced in ECS 3.8.0.1 that allows you to copy objects from Dell ECS to MinIO which is rather popular with customers and prospects who are modernizing their storage stack to support their AI data infrastructure requirements.
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Streamline your data processing capabilities, ensuring high-quality data management and secure operations. This integration not only enhances workflow automation but also leverages the advanced functionalities of MinIO and Tailscale, providing a powerful solution for modern data processing needs.
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Whether you are on-prem or in the Cloud, you want to ensure in the cloud operating model processes are set up in a homogenous way. This tutorial will give you a full overview of how you can surface MinIO audit logs in ElasticSearch so they can be searchable.
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In my previous post on MLRun, we set up a development machine with all the tools needed to experiment with MLRun. Specifically, we used a docker-compose file to create containers for the MLRun UI, the MLRun API Service, Nuclio, MinIO, and a Jupyter service. Once our containers started, we ran a simple smoke test to ensure everything was working correctly.
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MinIO’s co-founder and CEO AB Periasamy was recently featured on the AI in Business Podcast where he had a rich conversation with Matthew DeMello—Senior Editor at Emerj—about AI infrastructure and object storage for enterprises. In this blog post, we take you through an abridged version of what was discussed. Let’s get into it. AB and Matthew
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MLOps is to machine learning what DevOps is to traditional software development. Both are a set of practices and principles aimed at improving collaboration between engineering teams (the Dev or ML) and IT operations (Ops) teams. The goal is to streamline the development lifecycle, from planning and development to deployment and operations, using automation. One of the primary benefits of
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In this tutorial, we'll deploy a cohesive system that allows distributed SQL querying across large datasets stored in Minio, with Trino leveraging metadata from Hive Metastore and table schemas from Redis.
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When a MinIO Modern Datalake deployment is extended by adding a new server pool, by default it does not rebalance objects. Lets dive deep and learn how to rebalance smoothly without affecting cluster operations.
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Implementing KES within Kubernetes in a stateful configuration ensures the persistence of encryption keys through pod lifecycle events and restarts. This setup offers resilience especially in environments where relying on external KMS is not an option or preferred.
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Discover how to seamlessly migrate from HDFS to modern object storage without ripping out all of your current systems. Learn valuable strategies to retain essential tools and modernize your infrastructure for AI/ML.
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Delve into AI’s next frontier with MinIO S3 Object-Store and SDK, enhancing a Weaviate Retreival Augmented Generation (RAG) Pipeline for robust data management. Discover how to elevate efficiency in AI systems using LangChain, unlocking new dimensions in scalable AI solutions.
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If you are implementing a generative AI solution using Large Language Models (LLMs), you should consider a strategy that uses Retrieval-Augmented Generation (RAG) to build contextually aware prompts for your LLM. An important process that occurs in the preproduction pipeline of a RAG-enabled LLM is the chunking of document text so that only the most relevant sections of a document
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In mid-2024, creating an AI demo that impresses and excites can be easy. Take a strong developer, some clever prompt experimentation, and a few API calls to a powerful foundation model and you can often build a bespoke AI bot in an afternoon. Add in a library like langchain or llamaindex to augment your LLM with a bit of custom
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Explore the integration of SingleStore, a high-performance cloud-native database, with MinIO in the Modern Datalake Stack. This tutorial provides hands-on experience in data storage, processing, and querying, fostering experimentation and innovation in data management, analytics, and AI workloads.
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Explore the exciting possibilities of leveraging MinIO and LangChain to create a robust and efficient agent capable of handling complex data processing tasks.
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It's been just over a week for me here at MinIO. The big takeaway from immersing myself in whiteboarding sessions, architecture reviews and customer calls is that the simplicity of the product is both its distinguishing feature and one of its most defining value drivers. This is particularly true at scale. The explosive growth in computing power due
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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
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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
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MinIO’s high-performance object storage is key for AI innovation, offering scalability and integration for feature stores. Its capabilities enable seamless ML workflows, enhancing data management for AI development and deployment, impacting sectors like e-commerce and healthcare.
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As the computing world has evolved and the price of DRAM has plummeted, we find that server configurations often come with 500GB or more of DRAM. When you are dealing with larger deployments, even those with ultra-dense NVMe drives, the number of servers multiplied by the DRAM on those servers can quickly add up – often to several TBs. That DRAM
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