25-Part Series

MLOps for DevOps Engineers

A 25-part series bridging DevOps skills to MLOps. Same mindset, different artifacts.

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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.

4 min MLOps DevOps MLflow

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.

3 min MLOps MLflow Model Registry

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.

3 min MLOps DevOps Model Deployment

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.

2 min MLOps DVC Data Version Control

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.

2 min MLOps KServe Kubernetes

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.

2 min MLOps KServe Canary

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.

2 min MLOps KServe Kubernetes

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.

2 min MLOps KServe Cost Optimization

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.

2 min MLOps SHAP Explainability

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.

2 min MLOps Monitoring Prometheus

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.

2 min MLOps Data Drift Evidently

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.

2 min MLOps Retraining Pipelines

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.

2 min MLOps A/B Testing KServe

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.

2 min MLOps Kubeflow Kubernetes

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.

2 min MLOps Feature Store Feast

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.

2 min MLOps MLflow Governance

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.

2 min MLOps CI/CD GitHub Actions

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.

2 min MLOps Cost Optimization KServe