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.
Sandip Das
Senior Cloud, DevOps, MLOps & ML Platform Engineer | Heading Cloud, DevOps & MLOps for start-ups | AWS Container Hero | Educator | Mentor | Teaching Cloud, DevOps & Programming in Simple Way
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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.
Introduction to AWS SageMaker Training Jobs
Running ML training in the cloud with SageMaker.
Cloud Storage for ML: S3 and GCS Basics
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.
Introduction to Cloud Platforms for MLOps
Overview of AWS, GCP, and Azure ML services.
Serving Models with MLflow Models Serve
Move from experimentation to production by spinning up a REST API for your trained model with a single MLflow command. Learn mlflow models serve, model signatures, input schemas, and how to send real-time predictions.