https://stacksimplify.com/tags/devops/Recent content in DevOps on StackSimplify | DevOps & Cloud Education by Kalyan ReddyHugo -- gohugo.ioen-usTue, 14 Apr 2026 00:00:00 +0000https://stacksimplify.com/blog/ml-deployment-checklist/Tue, 14 Apr 2026 00:00:00 +0000https://stacksimplify.com/blog/ml-deployment-checklist/A data scientist hands you a model.pkl and says “deploy this.” What do you ask? Most engineers jump straight to containers and endpoints. But the questions that save you at 2 AM are the ones you ask before deployment, not during an incident. The Checklist # Question Why It Matters 1 What input will break it? Models return garbage confidently on bad input 2 What’s the rollback plan? “Redeploy the old one” is not a plan 3 How do we know it’s broken?https://stacksimplify.com/blog/devops-thinking-mlops-tools/Tue, 14 Apr 2026 00:00:00 +0000https://stacksimplify.com/blog/devops-thinking-mlops-tools/If you’re a DevOps engineer and a data scientist has ever handed you a model.pkl and said “deploy this”, you know the feeling. Where did this come from? What data trained it? Which version is this? How do I scale it? Here’s what I’ve learned after months building MLOps pipelines: these aren’t new problems. We’ve already solved them in DevOps. The tools are different, but the thinking is identical. The Mental Model: Same Problems, Different Artifacts Every MLOps challenge maps directly to a DevOps pattern you already understand:https://stacksimplify.com/blog/dvc-data-version-control/Tue, 14 Apr 2026 00:00:00 +0000https://stacksimplify.com/blog/dvc-data-version-control/You version code with Git. What about your model training data? If you’ve ever asked “Which dataset trained this model?” or “Can we reproduce last month’s model exactly?”, you need DVC. What DVC Solves Problem Without DVC With DVC Which dataset trained this model? “Check the shared drive, maybe?” git log shows exact data version Someone changed the training data No history, no diff dvc diff shows exactly what changed Reproduce last month’s model Impossible git checkout + dvc checkout Your Weekend Starter Six commands.https://stacksimplify.com/blog/feature-stores-ml/Tue, 14 Apr 2026 00:00:00 +0000https://stacksimplify.com/blog/feature-stores-ml/Your training pipeline computes “average transaction amount” as the mean of the last 30 days. Your inference API computes it as the mean of the last 7 days. Same feature name. Different values. Your model is silently wrong. This is training-serving skew. The number one silent killer of ML models in production. The Problem ML features get computed in two places: Context How Features Are Computed Problem Training Batch job on historical data, saved to CSV Code written by data scientist Serving API computes on the fly per request Different code, different logic Two separate implementations.https://stacksimplify.com/blog/ml-retraining-pipelines/Tue, 14 Apr 2026 00:00:00 +0000https://stacksimplify.com/blog/ml-retraining-pipelines/Your drift detector triggered an alert. Now what? Most teams freeze. The runbook says “retrain the model.” Nobody knows how. Monitoring without a retraining pipeline is like alerting without a runbook. The Retraining Spectrum Level Trigger Best For Manual Data scientist retrains in a notebook Small teams, low-risk models Scheduled Cron job retrains every week/month Predictable drift patterns Triggered Drift detector kicks off pipeline automatically High-value models Most teams should start with manual.https://stacksimplify.com/blog/mlflow-model-lifecycle/Tue, 14 Apr 2026 00:00:00 +0000https://stacksimplify.com/blog/mlflow-model-lifecycle/How does an ML model actually get from training to production? If you’re a DevOps engineer stepping into MLOps, MLflow is the first tool you need to understand. It handles the entire lifecycle: tracking experiments, versioning models, and serving them in production. The 5-Step Lifecycle Here’s the full journey of a model, from code to production. Step What Happens DevOps Analogy Experiment Write training code, MLflow creates a “run” Starting a CI build Run Logs parameters, metrics, model files Build artifacts + test results Model Best run registered to Model Registry Pushing image to Container Registry Registry Versions (v1, v2, v3) with aliases (@champion, @candidate) Image tags (:latest, :staging, :prod) Serving API loads models:/fraud-detector@champion K8s Deployment pulling :prod tag Step 1: Experiment You write training code and run it.https://stacksimplify.com/blog/mlops-series/Mon, 01 Jan 0001 00:00:00 +0000https://stacksimplify.com/blog/mlops-series/