Share common code and dependencies across Lambda functions to reduce deployment size.
Tag: DevOps
Tips and Tricks – Use Terraform Modules for Reusable Infrastructure
Create reusable infrastructure components with Terraform modules for consistency and DRY code.
Tips and Tricks – Use Multi-Stage Docker Builds for Smaller Images
Reduce container image size by separating build and runtime stages.
Tips and Tricks – Use Generators for Memory-Efficient Data Processing
Process large datasets without loading everything into memory using Python generators.
MLOps vs LLMOps: A Complete Guide to Operationalizing AI at Enterprise Scale
Understand the critical differences between MLOps and LLMOps. Learn prompt management, evaluation pipelines, cost tracking, and CI/CD patterns for LLM applications in production.
The IDE Wars Are Over: How Visual Studio 2025 and Modern Developer Tools Changed Everything
Remember when developers would argue passionately about whether Visual Studio, VS Code, JetBrains, or Vim was the “right” choice? Those debates feel almost quaint now. After two decades of watching IDE evolution—from the heavyweight Visual Studio 2003 that could barely run on 512MB of RAM to today’s AI-powered development environments—I can confidently say we’ve entered… Continue reading
Security as Code: Why DevSecOps Is No Longer Optional in 2025
The traditional approach to security—treating it as a final checkpoint before deployment—has become a liability in modern software delivery. After two decades of building enterprise systems, I’ve witnessed the painful evolution from “security as an afterthought” to “security as code.” In 2025, DevSecOps isn’t just a best practice; it’s a survival requirement for any organization… Continue reading
Mastering Google Cloud Platform: A Complete Architecture Guide for Enterprise Developers
Introduction: Google Cloud Platform has emerged as a formidable player in the enterprise cloud landscape, offering a unique combination of cutting-edge infrastructure, data analytics capabilities, and machine learning services that distinguish it from AWS and Azure. This comprehensive guide explores GCP’s core architecture patterns, enterprise design principles, and production-ready implementations using Terraform and Python. After… Continue reading
MLOps Best Practices: Building Production Machine Learning Pipelines That Scale
Master MLOps practices for production machine learning systems. Learn data versioning, experiment tracking with MLflow, CI/CD for ML, model registry governance, and monitoring strategies for AWS, Azure, and GCP.
Mastering GKE: A Deep Dive into Google Kubernetes Engine for Production Workloads
Introduction: Google Kubernetes Engine represents the gold standard for managed Kubernetes, built on the same infrastructure that runs Google’s own containerized workloads at massive scale. This deep dive explores GKE’s enterprise capabilities—from Autopilot mode that eliminates node management to advanced features like workload identity, binary authorization, and multi-cluster service mesh. After deploying production Kubernetes clusters… Continue reading