The first time I watched a security vulnerability slip through our CI/CD pipeline and make it to production, I felt the same sinking feeling every engineer knows: that moment when you realize the system you trusted has a blind spot. It was 2019, and we had what we thought was a mature DevOps practice. Automated… Continue reading
Tag: Kubernetes
DIY LLMOps: Building Your Own AI Platform with Kubernetes and Open Source
Build a production-grade LLMOps platform using open source tools. Complete guide with Kubernetes deployments, GitHub Actions CI/CD, vLLM model serving, and Langfuse observability.
Azure Container Apps: A Solutions Architect’s Guide to Serverless Containers
The evolution of container orchestration has reached an inflection point where the complexity of managing Kubernetes clusters often overshadows the benefits of containerization itself. Azure Container Apps represents Microsoft’s answer to this challenge, providing a serverless container platform that abstracts away infrastructure management while retaining the flexibility that modern cloud-native applications demand. Having architected numerous… 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
Azure Kubernetes Service (AKS): A Solutions Architect’s Guide to Enterprise Container Orchestration
After two decades of deploying and managing containerized workloads across enterprises, I’ve watched Kubernetes evolve from a complex orchestration tool into the de facto standard for container management. Azure Kubernetes Service (AKS) represents Microsoft’s fully managed Kubernetes offering, and having architected dozens of AKS deployments, I can share the patterns and practices that separate successful… Continue reading
Platform Engineering: Building Internal Developer Platforms That Actually Work
After spending two decades building and scaling engineering organizations, I’ve come to a conclusion that might seem counterintuitive: the biggest productivity killer in most enterprises isn’t technical debt, legacy systems, or even organizational politics. It’s cognitive load. Developers spend an unconscionable amount of time navigating infrastructure complexity instead of solving business problems. Platform engineering, done… Continue reading
Enterprise Generative AI: A Solutions Architect’s Framework for Production-Ready Systems
After two decades of building enterprise systems, I’ve witnessed numerous technology waves—from SOA to microservices, from on-premises to cloud-native. But nothing has matched the velocity and transformative potential of generative AI. The challenge isn’t whether to adopt it; it’s how to do so without creating technical debt that will haunt your organization for years. The… Continue reading
Cloud-Native Machine Learning: Building Scalable Models for Production
The journey from experimental machine learning models to production-grade systems represents one of the most challenging transitions in modern software engineering. After spending two decades building distributed systems and watching countless ML projects struggle to move beyond proof-of-concept, I’ve developed a deep appreciation for cloud-native approaches that treat machine learning infrastructure with the same rigor… Continue reading