C4: Container, Code, Cloud & Context
Designing intelligent systems, one layer at a time. ~ Nithin Mohan T K
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 […]
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Introduction: Amazon Elastic Container Registry (ECR) is a fully managed container registry service provided by AWS. It enables developers to store, manage, and deploy Docker container images securely. In this guide, we’ll explore how to provision a new AWS ECR using Terraform, a popular Infrastructure as Code (IaC) tool. We’ll cover not only the steps […]
Read more →Introduction: Python 3.12, released in October 2023, delivers significant improvements to error messages, f-string capabilities, and type system features. This release introduces per-interpreter GIL as an experimental feature, paving the way for true parallelism in future versions. After adopting Python 3.12 in production data pipelines, I’ve found the improved error messages dramatically reduce debugging time […]
Read more →In the fast-paced world of technology, ensuring the reliability of services is paramount for businesses to thrive. Site Reliability Engineering (SRE) has emerged as a discipline that combines software engineering and systems administration to create scalable and highly reliable software systems. In the Azure cloud environment, Azure Kubernetes Service (AKS) plays a pivotal role in […]
Read more →Introduction: Amazon Bedrock is AWS’s fully managed service for building generative AI applications with foundation models. Launched at AWS re:Invent 2023, Bedrock provides a unified API to access models from Anthropic, Meta, Mistral, Cohere, and Amazon’s own Titan family. What sets Bedrock apart is its deep integration with the AWS ecosystem, including built-in RAG with […]
Read more →Introduction: Processing thousands of LLM requests efficiently requires batch processing strategies that maximize throughput while respecting rate limits and managing costs. Individual API calls are inefficient for bulk operations—batch processing enables parallel execution, request queuing, and optimized resource utilization. This guide covers practical batch processing patterns: async concurrent execution, request queuing with backpressure, rate-limited batch […]
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