Build a Distributed Embedding Subsystem with MinIO, Langchain, and Ray Data

Build a Distributed Embedding Subsystem with MinIO, Langchain, and Ray Data

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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Bringing ARM into the AI Data Infrastructure Fold at MinIO Using SVE

Bringing ARM into the AI Data Infrastructure Fold at MinIO Using SVE

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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Data-Centric AI with Snorkel and MinIO

Data-Centric AI with Snorkel and MinIO

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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The Architects Guide to Machine Learning Operations (MLOps)

The Architects Guide to Machine Learning Operations (MLOps)

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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The Architect’s Guide to the GenAI Tech Stack - Ten Tools

The Architect’s Guide to the GenAI Tech Stack - Ten Tools

This post first appeared on The New Stack on June 3rd, 2024. I previously wrote about the modern data lake reference architecture, addressing the challenges in every enterprise — more data, aging Hadoop tooling (specifically HDFS) and greater demands for RESTful APIs (S3) and performance — but I want to fill in some gaps.  The modern data lake, sometimes referred to as

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WARP speed your AI data storage Infrastructure

AJ AJ on AI/ML |
WARP speed your AI data storage Infrastructure

Do you know the secret to some of the best AI models out there? It's the amount of data they had access to on which they could be trained on. For AI/ML models Fast accessible Data is King. Let me emphasize, it's not just Data, but fast accessible Data.

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Dell ECS Data Movement to MinIO

AJ AJ on Cloud Repatriation |
Dell ECS Data Movement to MinIO

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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MinIO Audit Logs in ElasticSearch in Kubernetes

AJ AJ on AI/ML |
MinIO Audit Logs in ElasticSearch in Kubernetes

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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Essentials for AI Infrastructure—the AI in Business Podcast with AB Periasamy and Matthew DeMello

Essentials for AI Infrastructure—the AI in Business Podcast with AB Periasamy and Matthew DeMello

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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Setting Up A Development Machine with MLRun and MinIO

Setting Up A Development Machine with MLRun and MinIO

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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Stateful KES for AI/ML Workloads

AJ AJ on AI/ML |
Stateful KES for AI/ML Workloads

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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