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APPLIED LEARNING · ENTERPRISE · EVIDENCE

Turn cloud and AI skills into useful work.

Choose a pathway, use curated external resources and build practical evidence. Technical specialisation is optional: every pathway connects technology choices to real problems, responsible use and sustainable enterprise.

Text-first learning plans work without streaming video and can be downloaded for offline reference.

Foundation learners, nontechnical founders and staff

Digital, cloud and responsible-AI foundations

Explain cloud costs, protect information and choose appropriate digital tools.

Practical work and evidence
  1. Complete an introductory cloud learning activity.
  2. Map the information used by a small enterprise and identify access risks.
  3. Compare a manual process with a digital alternative, including ongoing cost.
  4. Evaluate an AI answer against a reliable source and explain its limitations.

Portfolio evidence: A digital-readiness brief and a reviewed practical exercise.

  • AWS EducateExternal introductory learning and hands-on training resources.
  • AWS Skill BuilderExplore free learning first. Optional paid subscriptions require a separate decision.
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Technical learners and venture teams

Cloud application building

Build a small application with scoped access, reproducible deployment and a cost estimate.

Practical work and evidence
  1. Define a real user problem and a testable prototype.
  2. Build in a training sandbox, never the production account.
  3. Document permissions, data handling, backup and rollback.
  4. Present a working demonstration, source code and estimated monthly cost.

Portfolio evidence: A reviewed prototype, repository, operating guide and cost estimate.

  • AWS EducateExternal introductory learning and hands-on training resources.
  • AWS Skill BuilderExplore free learning first. Optional paid subscriptions require a separate decision.
  • AWS microcredentialsCheck current assessment availability and requirements directly with AWS.
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Researchers, programme staff and selected learners

Data, AI and evidence

Produce a reproducible analysis or evaluated AI application using authorised data.

Practical work and evidence
  1. State a research question and identify lawful, appropriately licensed data.
  2. Document consent or the applicable authority, limitations and retention.
  3. Create a reproducible analysis with denominators and quality checks.
  4. Evaluate accuracy and subgroup limitations without claiming causation from a dashboard alone.

Portfolio evidence: An analysis notebook or report, evaluation results and a data-management note.

  • AWS Skill BuilderExplore free learning first. Optional paid subscriptions require a separate decision.
  • AWS Open DataCheck each dataset licence and suitability before reuse.
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Founders and organisational clients

Cloud-enabled enterprise development

Develop a sustainable digital operating plan rather than an unnecessary technology stack.

Practical work and evidence
  1. Validate a customer problem and identify the minimum useful product.
  2. Define account ownership, customer-data boundaries and supplier responsibilities.
  3. Estimate unit costs and explain the operating model after credits expire.
  4. Present a prototype, customer-validation evidence and an accountable handover.

Portfolio evidence: A venture technology plan, validation record and operating budget.

  • AWS Skill BuilderExplore free learning first. Optional paid subscriptions require a separate decision.
  • AWS ActivateIndependent ventures must meet AWS eligibility requirements; credits are not transferred from Ubunifu.
Download text learning plan

Understand the credential

A Ubunifu completion record, an external learning badge, an AWS microcredential and AWS Certification are different achievements. Downloading a plan or completing a resource does not automatically confer certification. External accounts, assessments and eligibility remain with the provider.

These are independent Ubunifu pathways. They do not represent AWS Academy, AWS re/Start or AWS Training Partner membership.

Learn safely

Start with free training resources. Do not place private learner, customer or venture information in public AI tools. Use training environments rather than production accounts, and check the cost before starting any paid resource.

AI learning support

The course-grounded AI tutor is not activated. Course approval, model and region review, access controls and spending limits must be verified before learner information is submitted.