Kubernetes 1.35, released in January 2026 and now supported on Amazon EKS and EKS Distro, marks a significant milestone in container orchestration—particularly for AI/ML workloads. This release introduces In-Place Pod Resource Updates, allowing you to resize CPU and memory without restarting pods, and Image Volumes, a game-changer for delivering large AI models using OCI container […]
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Case Study: Building a Modern FHIR Patient Timeline Explorer with .NET 10 and React 19
Executive Summary This case study explores the development of DooLittle Health Patient Timeline Explorer, a modern healthcare application that demonstrates enterprise-grade architecture patterns for FHIR-compliant patient data visualization. Built as a proof-of-concept, this project showcases best practices in full-stack development, cloud-native deployment, and healthcare interoperability standards. 🏥 HEALTHCARE INTEROPERABILITY SERIES This article is part of […]
Read more →Production Model Deployment Patterns: From REST APIs to Kubernetes Orchestration in Python
After deploying hundreds of ML models to production across startups and enterprises, I’ve learned that model deployment is where most AI projects fail. Not because the models don’t work—but because teams underestimate the engineering complexity of serving predictions reliably at scale. This article shares production-tested deployment patterns from REST APIs to Kubernetes orchestration. 1. The […]
Read more →Security as Code: Why the Best DevSecOps Teams Treat Vulnerabilities Like Bugs, Not Afterthoughts
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 […]
Read more →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.
Read more →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.
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