C4: Container, Code, Cloud & Context
Designing intelligent systems, one layer at a time. ~ Nithin Mohan T K
Cloud computing is Internet-based computing, whereby shared resources, software, and information are provided to computers and other devices on demand, as with the electricity grid.
Cloud computing is a natural evolution of the widespread adoption of virtualization, Service-oriented architecture and utility computing. Details are abstracted from consumers, who no longer have need for expertise in, or control over, the technology infrastructure “in the cloud” that supports them.[1] Cloud computing describes a new supplement, consumption, and delivery model for IT services based on the Internet, and it typically involves over-the-Internet provision of dynamically scalable and often virtualized resources.[2][3] It is a byproduct and consequence of the ease-of-access to remote computing sites provided by the Internet.[4] This frequently takes the form of web-based tools or applications that users can access and use through a web browser as if it were a program installed locally on their…
Introduction: Amazon Elastic Kubernetes Service (EKS) simplifies the process of deploying, managing, and scaling containerized applications using Kubernetes on AWS. In this guide, we’ll explore how to provision an AWS EKS cluster using Terraform, an Infrastructure as Code (IaC) tool. We’ll cover essential concepts, Terraform configurations, and provide hands-on examples to help you get started […]
Read more →After two decades of architecting enterprise systems and spending the past year deeply immersed in Generative AI implementations, I can state with confidence that vector databases have become the cornerstone of modern AI infrastructure. If you’re building anything involving Large Language Models, semantic search, or Retrieval-Augmented Generation (RAG), understanding vector databases isn’t optional—it’s essential. This […]
Read more →Last year, I inherited a .NET AI application that was struggling. Response times averaged 2.3 seconds, costs were spiraling, and users were complaining. After three months of optimization, we cut latency by 87% and reduced costs by 72%. Here’s what I learned about optimizing .NET AI applications for production. Figure 1: .NET AI Performance Optimization […]
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After two decades of managing cloud infrastructure across enterprises of all sizes, I’ve witnessed the evolution of Infrastructure as Code from simple shell scripts to sophisticated declarative frameworks. AWS Cloud Development Kit (CDK) represents a paradigm shift that fundamentally changes how we think about infrastructure provisioning. Rather than wrestling with YAML or JSON templates, CDK […]
Read more →Six months ago, I faced a critical decision: build a custom ML model with ML.NET or use cloud APIs. The project required real-time fraud detection with zero latency tolerance. Cloud APIs were too slow. ML.NET was the answer. But when should you use ML.NET vs cloud APIs? After building 15+ production ML systems, here’s what […]
Read more →After two decades of architecting data systems across financial services, healthcare, and e-commerce, I’ve witnessed the evolution from batch-only processing to today’s sophisticated real-time architectures. The shift isn’t just about speed—it’s about fundamentally changing how organizations make decisions and respond to events. This article shares battle-tested insights on building production-grade real-time data processing systems in […]
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