Articles about storage, backup, virtualization, technology, innovation, programming, leadership, management,... etc
Articles about storage, backup, virtualization, technology, innovation, programming, leadership, management,... etc
Machine learning workloads demand a different infrastructure profile than general-purpose IT. Training, inference, and data preprocessing each stress compute, memory, storage, and network in distinct ways. Here’s how to build and tune a stack that keeps GPUs fed without wasting budget. 1. Assess Workload Requirements Identify the workload mix: training (high GPU, high bandwidth), inference […]
Block storage and file storage are two fundamental data storage architectures. They serve different use cases and have distinct characteristics: Block Storage Block storage stores data in fixed-size chunks called blocks. Each block has a unique identifier, and the operating system or application accesses them directly. There is no file system; the application or OS […]
As an IT manager responsible for storage and IT infrastructure, understanding the differences between SAN, NAS, and DAS is crucial for making informed decisions about your organization’s storage needs. Here’s a breakdown of the three storage technologies and guidance on selecting the best option for your use case: 1. DAS (Direct-Attached Storage) Definition: DAS refers […]
Hybrid AI/ML workloads run across on-premises infrastructure and public cloud. Here’s how to configure your environment for them. 1. Assess Your Requirements Workload Analysis Determine the type of AI/ML models you’ll run (training, inference, or both). Identify compute, storage, and networking needs. Decide whether you need GPUs, TPUs, or CPUs. Data Location Map where your […]
Automatic backups protect your data and enable disaster recovery. Here’s how to implement them. Step 1: Define Backup Requirements Identify what to back up: Data, applications, databases, system configurations, virtual machines. Prioritize by criticality. Set frequency: Daily, hourly, weekly — match the rate of change and business tolerance for data loss. Define retention: Keep backups […]
Building infrastructure for AI/ML pipelines means designing an environment that handles data processing, model training, inference, and storage without becoming a bottleneck. Here’s how to approach it. 1. Assess Requirements First Identify the actual workloads: training, inference, batch processing, real-time prediction. Each has different compute and latency profiles. Define scale. A prototype serving five users […]
