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)
Your models and datasets need a home that isn't your laptop. Today we set up S3 buckets, lock down access with IAM, move files with boto3, and connect DVC straight to the cloud. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 18)
Overview of AWS, GCP, and Azure ML services. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 17)
A complete implementation guide covering least-privilege access enforcement, Amazon Bedrock security hardening, continuous IAM permission auditing, and automated compliance evidence generation — built for production at scale.
Architecture, operations, performance tuning, and CLI commands for production-grade relational database deployments.
Example: Kubernetes, Terraform, Docker, AWS, MLOps...
