Kubernetes

Large Language Models (LLMs) are moving from research labs into production systems at an incredible pace. As soon as organizations move beyond simple API calls to third‑party providers, a question appears: “How do we run LLMs ourselves, reliably, and at scale?” For many teams, the answer is: Kubernetes. This article dives into Kubernetes for LLMs—when it makes sense, how to design the architecture, common pitfalls, and concrete configuration examples. The focus is on inference (serving), with notes on fine‑tuning and training where relevant. ...

In networking, ingress refers to traffic entering an organization’s network from external sources, while egress describes traffic leaving the network toward the outside world.[1][2] These concepts are foundational to cybersecurity, cloud architectures, and container orchestration, influencing everything from firewall rules to cost management.[1][4] Whether you’re a DevOps engineer managing Kubernetes clusters, a security professional designing defenses, or a cloud architect optimizing data flows, understanding ingress and egress is essential for secure, efficient systems. This comprehensive guide breaks down the definitions, contexts, security implications, and best practices, drawing from real-world applications in general networking, Kubernetes, VPNs, and cloud environments. ...

Python has become a powerhouse for building microservices due to its simplicity, vast ecosystem, and excellent frameworks like FastAPI, Flask, and gRPC. Microservices architecture breaks applications into small, independent services that communicate over networks, enabling scalability, faster development, and easier maintenance.[7] This guide provides a detailed walkthrough—from fundamentals to deployment—with practical code examples and curated resource links. What Are Microservices and Why Python? Microservices are self-contained applications that handle specific business functions, communicating via APIs (REST, gRPC) or message queues.[1][7] Unlike monoliths, they allow independent scaling and technology choices per service. ...

Introduction Microservices promise speed, scalability, and team autonomy by decomposing a system into small, independently deployable services. But they also introduce complexity in distributed systems, data consistency, and operational overhead. This in-depth, zero-to-hero guide walks you through microservices architecture from fundamentals to production-ready practices. You’ll learn when to choose microservices, how to design services and APIs, what tooling to adopt, and how to deploy, secure, and observe them at scale. Code snippets and reference patterns are included to bridge theory and practice. We end with curated resources for further study. ...

Introduction Kubernetes has become the de facto standard for running containerized applications at scale. But the ecosystem can feel overwhelming: pods, deployments, services, ingress, operators, Helm, RBAC—the list goes on. This in-depth tutorial takes you from zero to hero with clear explanations, hands-on examples, and practical guidance. You’ll learn the mental model, set up a local cluster, deploy and expose applications, manage configuration and storage, scale reliably, secure your workloads, observe and debug issues, and ship to production using modern workflows. ...