Articles about storage, backup, virtualization, technology, innovation, programming, leadership, management,... etc
Articles about storage, backup, virtualization, technology, innovation, programming, leadership, management,... etc
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 […]
Configuring GPU passthrough for virtual machines (VMs) allows you to dedicate a physical GPU to a VM, enabling high-performance workloads like AI, machine learning, video rendering, or gaming. Here’s a step-by-step guide to configure GPU passthrough, applicable to popular hypervisors such as VMware ESXi, Proxmox VE, or KVM/QEMU. Prerequisites: Hardware Requirements: A CPU and motherboard […]
Edge environments are more vulnerable than centralized data centers: they’re distributed, physically exposed, and run diverse endpoints. Here’s how to secure them. 1. Network security Use VPNs, TLS, or IPsec to protect traffic between edge devices, data centers, and cloud platforms. Adopt Zero Trust: every device, user, and application must authenticate and be verified. Segment […]
Optimizing IT infrastructure for real-time analytics means balancing performance, scalability, reliability, and cost. Here are the key areas to address: 1. Assess Requirements Measure your actual data ingest rates: volume, velocity, and variety. Define latency thresholds your analytics can tolerate. Profile workloads to size CPU, GPU, memory, storage, and network. 2. Upgrade Hardware Infrastructure Select […]
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 […]
