Faster, smaller Docker images: practical patterns with BuildKit, Buildx cache and minimal base images
Building Docker images is part craft, part engineering: you want fast, reproducible builds in CI and tiny, secure images in production. Over the last few years the tooling around Docker...
Bringing AI to Logs: How embeddings, LLMs, and modern observability tools help detect infrastructure issues earlier
Infrastructure teams face an ever-growing firehose of logs: application traces, system events, kernel messages, load balancer access logs, and agent telemetry. That volume and variety make it hard to surface...
Ephemeral GPU workloads: running scalable ML training with Airflow on Kubernetes
Bridging DevOps and MLOps means giving model training the same repeatable, observable, and automated workflows we expect from data pipelines. A practical, widely-adopted pattern is to run ML training and...
Migrating from Terraform to OpenTofu: a practical intro and step-by-step path
OpenTofu is the community-driven open-source fork of Terraform that emerged after Terraform’s licensing change. For teams who want to preserve an open, community-owned IaC toolchain while keeping most existing Terraform...
What DevSecOps Is and Why It Matters
DevSecOps is the practice of integrating security into every stage of the software delivery lifecycle so that development, security, and operations teams share responsibility for secure, reliable releases. Far from...
Serverless 101: Deploying your first AWS Lambda with a Function URL
Getting started with AWS Lambda usually means wiring an event source (API Gateway, S3, or a stream) and a function. For an introductory, low-friction path to an HTTP endpoint, Lambda...
Pods, Deployments, and Services: a friendly guide to how Kubernetes runs your app
Kubernetes can feel like a new language at first, but three concepts map tightly to real-world roles: Pods are the actual workers, Deployments are the manager who hires and replaces...
Scaling ML training with Airflow Dynamic Task Mapping and Kubernetes
Orchestrating machine learning pipelines is where classic DevOps tooling meets model-driven complexity. If you need many parallel training runs (hyperparameter sweeps, per-shard training, or large-scale feature engineering), combining Apache Airflow’s...