# Designing self-service ML infrastructure

[View the visual case study](https://joshdunne.design/work/aws-hyperpod)

Shared GPU infrastructure. Guided admin setup. A faster path from available compute to a working environment.

AWS SageMaker · 2024 to 2025

Role: Lead Product Designer and Design Engineer

Team: Solo designer with engineering and product

Duration: 8 months

Scope: Shipped admin experience and data scientist prototype

![SageMaker Spaces on HyperPod console with reusable development templates](https://joshdunne.design/_next/static/media/spaces-console.2dfc7635.webp)

The Spaces console brings reusable templates and active development environments into HyperPod.

## Outcomes

~99% Setup reduction. From 1 to 2 days down to 2 to 5 minutes

1:15 Template reuse. Templates could span namespaces and teams

3+ Enterprise customers. Adopted within 2 weeks of launch

13+ Spaces created. Active early usage after launch

## Two workflows. One GPU cluster.

Problem: Admins needed governance. Data scientists needed available compute.

Decision: I shipped the admin foundation and prototyped a hardware-first path for data scientists.

Tradeoff: Data scientists launched with CLI access while the GUI remained a prototype.

Shipped result: Admin setup fell from days to minutes.

## From manual setup to Quick Install

### Install the foundation

Admin workflow. Dependencies and installation progress stay visible.

[Watch video (0:13)](https://joshdunne.design/media/case-studies/aws-hyperpod/quick-install-v1.mp4)

![Install the foundation](https://joshdunne.design/_next/static/media/quick-install-v1.6e5e4720.webp)

Admin workflow. Dependencies and installation progress stay visible.

## See capacity. Then create.

### Find available compute

Data scientist prototype. Compare capacity by hardware or team.

[Watch video (0:20)](https://joshdunne.design/media/case-studies/aws-hyperpod/capacity-views-v1.mp4)

![Find available compute](https://joshdunne.design/_next/static/media/capacity-views-v1.bb233265.webp)

Data scientist prototype. Compare capacity by hardware or team.

### Create a Space

Data scientist prototype. Choose hardware, then configure the environment.

[Watch video (0:48)](https://joshdunne.design/media/case-studies/aws-hyperpod/create-space-v1.mp4)

![Create a Space](https://joshdunne.design/_next/static/media/create-space-v1.4f4e6d40.webp)

Data scientist prototype. Choose hardware, then configure the environment.

## A public milestone

AWS published the interactive IDE workflow in November 2025.

![AWS Machine Learning Blog article dated November 24, 2025.](https://joshdunne.design/_next/static/media/aws-blog-launch.4c7611ad.webp)

AWS published the interactive IDE workflow in November 2025.