ML Model inference Scaling on Kubernetes Using Horizontal Pod Autoscaling and Rolling Updates
Make ML Models running on Kubernetes survive a traffic spike, and ship a new model version without a single failed prediction
MLOps106
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Kubernetes ConfigMaps, Secrets, and Persistent Volumes for ML Models
Managing Application Configuration, Credentials, and Persistent Model Storage in Kubernetes
Deploying ML Models on Kubernetes
From Docker Containers to Production-Ready Kubernetes Deployments
Introduction to Kubernetes for MLOps Engineers
Kubernetes core concepts and architecture
Week 3 Review: The Cloud ML Workflow, End-to-End
Consolidate everything from cloud storage through SageMaker training to a live inference endpoint, then put a price tag on running it in production.
SageMaker Model Deployment & Endpoints
Take your trained machine learning models from the registry to production by deploying scalable, secure, and highly available real-time inference endpoints with Amazon SageMaker.