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.
By Sandip Das · · 8 min read
💡
Hey — It's Sandip Das 👋
Cloud object storage is the foundation of every production MLOps workflow, enabling secure, scalable, and durable storage for datasets, model artifacts, and experiment outputs.
Mastering Amazon S3 and Google Cloud Storage helps MLOps engineers build cloud-native ML pipelines that support collaboration, automation, versioning, and reproducible deployments.
Cloud object storage is the foundation of every production MLOps workflow, enabling secure, scalable, and durable storage for datasets, model artifacts, and experiment outputs.
Mastering Amazon S3 and Google Cloud Storage helps MLOps engineers build cloud-native ML pipelines that support collaboration, automation, versioning, and reproducible deployments.
📚 Key Learnings
- Your model can't live in
models/forever - S3 fundamentals: buckets, objects, keys
- IAM permissions: who can touch the bucket
- Versioning: every overwrite is recoverable
- Lifecycle policies: don't pay Standard rates forever
- GCS in five minutes
- Give
iris-mlopsa real remote
🧠 Learn here
Your model can't live in models/ forever
Back in Days 4-6 we set up DVC and pointed
Read the full article on learnxops.com.
Topics: 100 Days of MLOps, MLOps, ML, Community, Python, MLflow, Cloud, AWS, GCP