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.
By Sandip Das · · 11 min read
💡
Hey — It's Sandip Das 👋
Understanding the complete cloud ML workflow from storing datasets in cloud storage to training models in SageMaker, deploying production endpoints, and estimating infrastructure costs helps us build scalable, reliable, and cost-efficient machine learning systems that are ready for real-world production.
Understanding the complete cloud ML workflow from storing datasets in cloud storage to training models in SageMaker, deploying production endpoints, and estimating infrastructure costs helps us build scalable, reliable, and cost-efficient machine learning systems that are ready for real-world production.
📚 Key Learnings
- The mental model
- The end-to-end architecture
- Setup: IAM roles & project skeleton
- Step 1 — Upload data to S3
- Step 2 — SageMaker training job
- Step 3 — Register the model
- Step 4 — Deploy an endpoint
- Step 5 — Invoke predictions
- Production cost estimate
🧠 Learn here
Over the last few days you moved model serving out of your laptop and into managed cloud infrastructure. Before we wire it all together, let's re-anchor the three pillars.
Everything this week orbits a single idea: separate where data lives, where c Read the full article on learnxops.com. Topics: 100 Days of MLOps, MLOps, ML, Community, Python, MLflow, Cloud, AWS, GCP