KServe
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.
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.
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.
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.
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.