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AWS Education Australia

RMIT University

RMIT needed secure, high-performance cloud research environments that could handle large simulations, geospatial knowledge graphs, and AI workloads much faster than on-premises servers while making advanced infrastructure accessible to researchers.

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100x performance improvement
From 3 months to 3.5 days
over 2 million interlinked geospatial records processed in under an hour
over 550 researchers onboarded

Reported impact

Business outcome

RMIT onboarded more than 550 researchers, achieved up to 100x performance improvement for some workloads, cut aerospace solving time from nearly 3 months to 3.5 days, and processed more than 2 million interlinked geospatial records in under an hour.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

RMIT built its RACE research-computing ecosystem on AWS to give researchers secure, well-architected environments for HPC, data science, and emerging compute-intensive workloads. Amazon EC2 and AWS ParallelCluster provide the elastic compute backbone for simulation-heavy research, while Amazon SageMaker and Amazon SageMaker Studio support data-science and AI workflows, and Amazon Braket expands access to advanced experimentation. RACE packages these services with training, consultancy, and secure environment templates so researchers can test ideas quickly without building their own infrastructure from scratch. The result is a reusable cloud research platform that cuts solve times from months to days, enables very large aerospace and geospatial analyses, and improves funding leverage by giving more than 550 researchers access to on-demand high-performance computing.

Data flow

Research datasets and simulation jobs are provisioned through RACE into AWS ParallelCluster and Amazon EC2 environments, with downstream AI and analytics work supported in Amazon SageMaker and SageMaker Studio and broader experimentation enabled through Amazon Braket.

Solution architecture

5 components · 3 layers
  1. Compute
    • Amazon EC2 Provides elastic compute for large simulation and research workloads.
    • Amazon SageMaker Supports AI and machine-learning research workflows.
    • Amazon Braket Extends the platform to advanced compute and experimentation use cases.
  2. Serving
    • Amazon SageMaker Studio Gives researchers a unified environment for model and data-science work.
  3. Orchestration
    • AWS ParallelCluster Orchestrates high-performance computing environments for research teams.

Architecture clues

  • RACE provides a catalogue of secure, well-architected cloud environments plus training and consultancy.
  • Supports both HPC-style simulation workloads and knowledge-graph processing.
  • Uses pay-for-use cloud capacity instead of fixed research hardware.

Evidence from the source

The team’s solving time for analyses has reduced from nearly 3 months to just 3.5 days using RACE.
With over 550 researchers onboarded, RACE has facilitated rapid testing of ideas and solutions, with some use cases seeing a 100x performance improvement when compared to existing on-premises servers.
over 2 million interlinked geospatial records processed in under an hour
Transparency Evidence & methodology 95/100 reference value · 82% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence82%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence15/20
    3 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. AWS

    RMIT University customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/rmit-university/
    Source type
    Vendor-published customer story
    Published
    Sep 2, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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