3.4 Common Use Cases
Common Use Cases
This section will contain guides for common research scenarios using direct AWS access. Each use case will include a brief overview and links to relevant AWS documentation.
Compute (EC2)
Running virtual machines for research workloads.
Storage (S3)
Storing and sharing research datasets.
Machine Learning (SageMaker)
Training and deploying ML models.
Batch Processing (AWS Batch)
Running large-scale parallel workloads.
Generative AI (Bedrock)
Accessing foundation models for generative AI research.
Big Data (EMR / Athena)
Processing and querying large datasets.
Quick Service Selection Guide
| Need | Service |
|---|---|
| Run a virtual machine | EC2 |
| Store files/objects | S3 |
| Relational database | RDS |
| Machine learning | SageMaker |
| Batch processing | AWS Batch |
| Serverless compute | Lambda |
| Big data analytics | EMR, Athena |
| Generative AI | Bedrock |
| HPC clusters | PCluster |
Tip
Start with the simplest service that meets your needs. You can always migrate to more complex solutions as requirements grow.