Make ML Models running on Kubernetes survive a traffic spike, and ship a new model version without a single failed prediction (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 25)
Managing Application Configuration, Credentials, and Persistent Model Storage in Kubernetes (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 24)
From Docker Containers to Production-Ready Kubernetes Deployments (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 23)
Kubernetes core concepts and architecture (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 22)
Consolidate everything from cloud storage through SageMaker training to a live inference endpoint, then put a price tag on running it in production. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 21)
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. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 20)
Example: Kubernetes, Terraform, Docker, AWS, MLOps...
