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MLOps

4 min

ML Security on Kubernetes: 4 Layers Protecting Your Models

Your model endpoint has no auth. Anyone with the URL gets predictions. That is the default on most KServe deployments. Here are the 4 layers that fix it.

GPU Scheduling on Kubernetes: MIG, Time-Slicing, and Node Pools

One A100 GPU costs $3/hour. Your model uses 12% of it. Here is how GPU sharing on Kubernetes cuts ML infrastructure bills by 60% or more.

Batch vs Real-Time ML Inference: 90% of Predictions Can Be Batch

Your model runs in real-time. 90% of your predictions do not need to. Here is the decision framework and the cost math showing 99.5% savings.

5 Levels of ML Model Deployment on Kubernetes

From baked Docker images to explainable AI. Each level adds production capabilities. Here is the progression every DevOps engineer should know.

5 Questions to Ask Before Every ML Model Deployment

A data scientist hands you a model.pkl. Before deploying, ask these 5 production-ready questions every DevOps engineer should know.

A/B Testing for ML Models: When Offline Metrics Lie

You retrained the model. Accuracy went up 2% on the test set. Revenue dropped 5%. Here is why you need A/B testing for ML models.

Canary Deployments for ML Models with KServe and Istio

You do canary deployments for APIs. Why not for ML models? Here is how KServe and Istio split traffic between champion and candidate models.

CI/CD for ML: Same GitHub Actions, Different Artifact

Your CI/CD pipeline deploys code. Ours deploys models. Same tools: GitHub Actions, ArgoCD, Docker, DVC, MLflow. Here is the 7-job ML pipeline.

Data Drift Detection: When Your Model Stops Being Right

Your model was trained on last year's data. The world moved on. Here are the 3 types of drift and how to detect them with Evidently AI.

DevOps Thinking Applied to MLOps: 5 Essential Tools

You already know 80% of MLOps. Here are 5 open-source tools that map directly to your existing DevOps skills.

DVC: Git for Your ML Training Data

You version code with Git. DVC does the same for ML training data. Here is your weekend starter guide to data version control.

Feature Stores: The Package Registry for ML Features

Your training pipeline computes 'average amount' as 30-day mean. Your API computes it as 7-day mean. Same name, different values. Feature stores fix this.

ML Cost Optimization: One YAML Field Cut Our Bill by 80%

We changed minReplicas from 1 to 0. Infrastructure cost dropped 80%. Here is how KPA, scale-to-zero, and panic mode work for ML inference.

MLOps Cost Optimization

Apr 14, 2026

MLOps

2 min

ML Governance: The Champion-Challenger Pattern for Model Deployment

Your ML serving code should never know version numbers. The champion-challenger pattern with MLflow aliases gives instant rollback.

ML Model Monitoring: Your Grafana Dashboard Is Lying to You

Your model uses 10% CPU, zero errors, healthy pod status. And still returns garbage predictions. Here are the 3 alerts you need today.

ML Pipeline Orchestration with Kubeflow on Kubernetes

Your ML team has 47 Jupyter notebooks. 12 should run in order. Nobody remembers which 12. Kubeflow Pipelines fixes this on your existing K8s cluster.

ML Retraining Pipelines: From Drift Alert to Production Model

Your drift detector triggered. Now what? Here is the retraining pipeline every MLOps team needs, with quality gates to prevent deploying garbage.

MLflow in 60 Seconds: The Complete ML Model Lifecycle

From training to production in 5 steps. How MLflow tracks experiments, versions models, and enables instant rollbacks with zero code changes.

Scale-to-Zero for ML Models: Stop Paying for Idle Compute

Your ML model runs 24/7. Inference requests come 2% of the time. KServe plus Knative scales to zero when idle. Here is how.

SHAP Explainability: Why Your ML Model Flagged That Transaction

GDPR requires explanations for automated decisions. SHAP values tell you exactly why your model made each prediction. Here is how KServe serves explanations.

The Two-Container Pattern: Transformer + Predictor for ML Serving

Your ML model expects clean features. Your API receives raw data. The two-container pattern with KServe solves this with clear separation of concerns.

Quality Gates for ML: 4 Layers Between Training and Production

40% of candidate models got rejected at the quality gate. That is not a failure rate. That is a protection rate. Four layers that stop bad models.

5 Things I Wish I Knew Before Running EKS in Production

Hard-won lessons from running Amazon EKS in production — from Karpenter node consolidation to OpenTelemetry observability and real AWS database integrations.

Building a Complete Observability Stack for EKS with OpenTelemetry and ADOT

How to set up production-grade observability on Amazon EKS using AWS Distro for OpenTelemetry (ADOT) with three separate collectors for traces, logs, and metrics.