Category: Expert (400)

Machine learning (ML) models used for inference on Kubernetes are often several gigabytes in size. When these models are embedded in container images, images become oversized and pod scheduling slows. More critically, inference pods are inherently stateful. Model weights, tokenizer files, compiled GPU kernels, and runtime caches must persist across pod restarts, node failures, and […]

As data volumes grow, organizations must move large datasets between storage locations to meet compliance requirements, optimize performance, and help meet data sovereignty requirements. However, migrating petabytes of data presents significant challenges: lengthy transfer times, complex coordination of parallel operations, data integrity verification, and substantial engineering overhead. Automation reduces resource consumption and operational complexity during […]

Organizations routinely need to move a subset of their stored data from one location to another, such as a compliance audit that requires all PDFs from a specific quarter, a company reorganization that splits one tenant’s records into an isolated bucket, or a regulatory mandate that demands financial documents older than 7 years be archived […]

Organizations across industries use signed URLs to grant temporary, credential-less access to private resources such as receipts, medical or financial records, legal files, or confidential reports. However, signed URLs can be reused by anyone until they expire, creating security risks if a URL is shared or inadvertently disclosed. This risk can be mitigated by vending […]

When customers choose to run self-managed Oracle databases in the cloud, one of the fundamental decisions they make is where to store their data. Although many deployments use locally attached block storage, there’s a compelling alternative: running Oracle on a network file system such as Amazon FSx for OpenZFS. Shifting database storage from local disks […]

Users love the simplicity of AWS Fargate for running containerized workloads without managing servers, scaling infrastructure, or worrying about underlying compute capacity. As users expand their Fargate adoption, they increasingly want to run applications that require block data storage – content management systems like WordPress that retain uploaded media and plugin configurations, retail applications that […]

Modern containerized applications running on Kubernetes enable organizations to deploy read-heavy workloads—such as machine learning inference, data analytics, and High Performance Computing (HPC)—at unprecedented scale. However, when multiple pods on the same node need to access identical datasets, a performance challenge emerges. Each pod typically fetches files from external storage independently over the network. This […]

As enterprises scale their adoption of generative AI, one of the biggest technical challenges is connecting AI applications to the right data and making that data fast, accessible, and secure. AI agents are transforming industries through applications like customer support automation, personalized e-commerce recommendations, and research assistance in financial services and healthcare. These applications require […]

In the cloud-native landscape, Amazon Elastic Block Store (Amazon EBS) volumes serve as the backbone for persistent storage in containerized applications. As organizations scale their Kubernetes workloads on Amazon Elastic Kubernetes Service (Amazon EKS), they increasingly rely on EBS volumes to provide high-performance, durable storage for stateful applications such as databases, message queues, and data […]

Retrieval-Augmented Generation (RAG) is a technique that optimizes large language model (LLM) outputs by referencing authoritative knowledge bases outside of the model’s training data before generating responses. This addresses common limitations of traditional LLMs, such as outdated knowledge, hallucinated facts, and misinterpreted terminology. Organizations can implement RAG to enhance their generative AI applications with current, […]