Cloud Learning Platform
Turning a complex training ecosystem into a strategic priority
A unified learning platform for a hyperscale cloud provider’s workforce. As the sole designer, I collaborated with a senior UX researcher and product manager to consolidate a disjointed training ecosystem into a cohesive, user-centered experience.
Client
A hyperscale cloud provider
Role
Product Designer
Team
Sr. UX researcher/PM
Industries
Enterprise
Timeline
2023-2024
Screens shown are recreated to illustrate design decisions and are not representative of the actual client UI.
Cloud Learning Platform
Turning a complex training ecosystem into a strategic priority
A unified learning platform for a hyperscale cloud provider’s workforce. As the sole designer, I collaborated with a senior UX researcher and product manager to consolidate a disjointed training ecosystem into a cohesive, user-centered experience.
Client
Amazon Web Services
Role
Product Designer
Team
Sr. UX researcher/PM
Industries
Enterprise
Timeline
2023-2024
Cloud Learning Platform
Screens shown are recreated to illustrate design decisions and are not representative of the actual client UI.
Client
A hyperscale cloud provider
Role
Product Designer
Team
Sr. UX researcher/PM
Industries
Enterprise
Timeline
2023-2024
A unified learning platform for a hyperscale cloud provider’s workforce. As the sole designer, I collaborated with a senior UX researcher and product manager to consolidate a disjointed training ecosystem into a cohesive, user-centered experience.
Turning a complex training ecosystem into a strategic priority
The problem
The hyperscale cloud provider’s global training program supports tens of thousands of learners across regions, roles, and seniority levels. While the content itself was robust, the system lacked structure:
❌ No shared starting point
❌ No clear progression path
❌ No single place to understand what the training ecosystem contained
From the outside, it appeared to be a system. From the inside, it felt like guesswork. The challenge wasn’t to rebuild everything but to define what a unifying product layer could look like and make the case for it.
Making sense of a complex system
As the sole designer, I worked closely with a UX researcher who led stakeholder management and learner interviews. Before defining any design direction, I immersed myself in extensive internal documentation to avoid assumptions. My research included:
🔹 Platform overviews
🔹 Tooling inventories
🔹 Team ownership maps
What emerged were three core issues:
❌ Fragmented entry points
❌ No shared proficiency baseline
❌ No personalised direction once learners were already inside the system
I reframed these into two foundational problems:
Learners didn’t know where they stood in their training journey.
Learners didn’t know what to do next.
Every design decision afterward revolved around reducing these two points of friction.
An exercise in restraint
The constraints shaped every design decision:
No full platform replacement: The solution had to integrate seamlessly with the existing system, sitting on top of it without disrupting the progress of learners already mid-course. A complete rebuild wasn’t feasible, so the focus was on creating an additive layer that improved the experience without altering what was already in place.
Rigid design system: The system’s tight, opinionated components limited visual flexibility. Clarity had to emerge from structure and logic rather than styling. This constraint meant every decision had to be defensible on its own merits, relying on sound reasoning rather than aesthetic adjustments.
The solution
My first instinct was to build all of it into one consolidated dashboard, a single screen that tried to resolve proficiency, recommendations, and guidance at once.
It felt efficient on paper. In practice it asked a learner to absorb three different kinds of decisions in one glance, which worked against the clarity I was trying to create.
I split it into three distinct flows instead:
Skills Assessment
Personalized Training Recommendations
Conversational Chatbot
Three-part learning model
📊 Skills Assessment
Established a shared baseline for proficiency.
Gave learners and the platform a clear understanding of their starting point.
🎯 Personalized Training Recommendations
Tied directly to the baseline assessment.
Filtered the full catalog to show only the most relevant options for the learner’s role and level.
💬 Conversational Chatbot
Provided on-demand guidance for navigating the complex ecosystem.
Allowed learners to ask questions in natural language and receive contextual suggestions.
A lightweight home view tied these flows together, showing learners where they stood and directing them to the most relevant flow for their needs.
Designing for engagement
Much of the training was mandatory, so the platform had to compete with low motivation. To address this, the researcher and I introduced:
🔹 An expertise score that moved as a learner progressed
🔹 A streak that rewarded consistency rather than one-off effort
🔹 A sense of where someone stood relative to their peers
These elements sat on top of the core product logic, making the experience feel rewarding and worth engaging with.
Designing for engagement
My Learning Hub
This section became the central hub for tracking progress and maintaining momentum. To ensure learners could easily resume their journey, we introduced:
Active courses: A dedicated space where learners could seamlessly pick up where they left off, eliminating the frustration of losing progress or forgetting their place in a course.
Certification paths: A visual representation of their journey toward completion, showing how far they’d come and what remained, turning abstract goals into clear, actionable steps.
Completed history: A record of their achievements, transforming streaks and scores into tangible milestones. This not only reinforced their progress but also made the learning experience feel rewarding and worth returning to.
Designing for engagement
The AI assistant followed the same principle. It started as a floating action button, something you summoned when needed and dismissed when not.
That framed it as a tool separate from the learning, external to the journey rather than part of it. Making it collapsible changed what it was. Present throughout but never forced into view, it became less of a chat bot and more of a guide that stayed with you as you moved through the platform, there when you needed it, out of the way when you didn't.
Impact
The work was presented to a VP, and the initiative was elevated from exploratory design work to a funded strategic priority.
From there, it transitioned to a dedicated product team in Seattle, who continued development from the foundation I handed over. I documented the end-to-end journey, the design rationale, and the product direction in enough detail that the incoming team could move forward without re-framing the problem or repeating the discovery I'd already done.
A first version of the platform launched in 2025, the first phase of a staged rollout. The work I designed, the assessment, the recommendations, the chatbot, was slated for a later phase, and I moved on to other projects before that phase went live. What I can speak to with confidence is the handover: the thinking held up well enough to be carried forward without anyone needing to start over.
The signal I trust most isn't the funding decision. It's that the Seattle team never had to reach out and ask what a decision meant, they built past where I left off without reopening discovery I had already done.
What I took away
I came into this project thinking my job was to design a platform. I left realising my job was to make a complex problem legible enough that other people could act on it, and the screens themselves were almost incidental to that.
What I was really building was shared understanding, of the problem, the constraints, and what was worth doing first. The handover is what proved that to me: understanding either transfers cleanly to someone else or it doesn't, and there's no partial credit for having it only in your own head.
Working without a dedicated team also meant holding the strategic and the tactical at the same time. The researcher was doing double duty as the de facto PM, which meant there was no separate product function to hand decisions off to, no design lead to check my reasoning against. Knowing when to go deep on craft and when to step back and question the brief is something I had to figure out without anyone else's judgment to lean on. That's the skill I keep reaching for.
Impact
The work was presented to a VP, and the initiative was elevated from exploratory design work to a strategic priority.
It was then transitioned to a dedicated product team, who continued development based on the foundation I provided.
I documented the end-to-end journey, design rationale, and product direction in enough detail that the incoming team could move forward without revisiting the problem or repeating discovery work. The first version of the platform launched in 2025 as the initial phase of a staged rollout.
While the full implementation of my designs (assessment, chatbot recommendations) was slated for a later phase, the handover itself was the proof of success: The new team never needed to ask for clarification on any decision.
What I took away
I came into this project thinking my job was to design a platform. I left realising my job was to make a complex problem legible enough that other people could act on it, and the screens themselves were almost incidental to that.
What I was really building was shared understanding, of the problem, the constraints, and what was worth doing first. The handover is what proved that to me: understanding either transfers cleanly to someone else or it doesn't, and there's no partial credit for having it only in your own head.
Working without a dedicated team also meant holding the strategic and the tactical at the same time. The researcher was doing double duty as the de facto PM, which meant there was no separate product function to hand decisions off to, no design lead to check my reasoning against. Knowing when to go deep on craft and when to step back and question the brief is something I had to figure out without anyone else's judgment to lean on. That's the skill I keep reaching for.
Making sense of a complex system
As the sole designer, I worked closely with a UX researcher who led stakeholder management and learner interviews. Before defining any design direction, I immersed myself in extensive internal documentation to avoid assumptions. My research included:
🔹 Platform overviews
🔹 Tooling inventories
🔹 Team ownership maps
What emerged were three core issues:
❌ Fragmented entry points
❌ No shared proficiency baseline
❌ No personalised direction once learners were already inside the system
I reframed these into two foundational problems:
Learners didn’t know where they stood in their training journey.
Learners didn’t know what to do next.
Every design decision afterward revolved around reducing these two points of friction.
An exercise in restraint
The constraints shaped every design decision:
No full platform replacement: The solution had to integrate seamlessly with the existing system, sitting on top of it without disrupting the progress of learners already mid-course. A complete rebuild wasn’t feasible, so the focus was on creating an additive layer that improved the experience without altering what was already in place.
Rigid design system: The system’s tight, opinionated components limited visual flexibility. Clarity had to emerge from structure and logic rather than styling. This constraint meant every decision had to be defensible on its own merits, relying on sound reasoning rather than aesthetic adjustments.
The solution
My first instinct was to build all of it into one consolidated dashboard, a single screen that tried to resolve proficiency, recommendations, and guidance at once.
It felt efficient on paper. In practice it asked a learner to absorb three different kinds of decisions in one glance, which worked against the clarity I was trying to create.
I split it into three distinct flows instead:
Skills Assessment
Personalized Training Recommendations
Conversational Chatbot
Three-part learning model
📊 Skills Assessment
Established a shared baseline for proficiency.
Gave learners and the platform a clear understanding of their starting point.
🎯 Personalized Training Recommendations
Tied directly to the baseline assessment.
Filtered the full catalog to show only the most relevant options for the learner’s role and level.
💬 Conversational Chatbot
Provided on-demand guidance for navigating the complex ecosystem.
Allowed learners to ask questions in natural language and receive contextual suggestions.
A lightweight home view tied these flows together, showing learners where they stood and directing them to the most relevant flow for their needs.
My Learning Hub
This section became the central hub for tracking progress and maintaining momentum. To ensure learners could easily resume their journey, we introduced:
Active courses: A dedicated space where learners could seamlessly pick up where they left off, eliminating the frustration of losing progress or forgetting their place in a course.
Certification paths: A visual representation of their journey toward completion, showing how far they’d come and what remained, turning abstract goals into clear, actionable steps.
Completed history: A record of their achievements, transforming streaks and scores into tangible milestones. This not only reinforced their progress but also made the learning experience feel rewarding and worth returning to.
My Learning Hub
This section became the central hub for tracking progress and maintaining momentum. To ensure learners could easily resume their journey, we introduced:
Active courses: A dedicated space where learners could seamlessly pick up where they left off, eliminating the frustration of losing progress or forgetting their place in a course.
Certification paths: A visual representation of their journey toward completion, showing how far they’d come and what remained, turning abstract goals into clear, actionable steps.
Completed history: A record of their achievements, transforming streaks and scores into tangible milestones. This not only reinforced their progress but also made the learning experience feel rewarding and worth returning to.
The AI assistant followed the same principle. It started as a floating action button, something you summoned when needed and dismissed when not.
That framed it as a tool separate from the learning, external to the journey rather than part of it. Making it collapsible changed what it was. Present throughout but never forced into view, it became less of a chat bot and more of a guide that stayed with you as you moved through the platform, there when you needed it, out of the way when you didn't.
The AI assistant followed the same principle. It started as a floating action button, something you summoned when needed and dismissed when not.
That framed it as a tool separate from the learning, external to the journey rather than part of it. Making it collapsible changed what it was. Present throughout but never forced into view, it became less of a chat bot and more of a guide that stayed with you as you moved through the platform, there when you needed it, out of the way when you didn't.
Impact
The work was presented to a VP, and the initiative was elevated from exploratory design work to a funded strategic priority.
From there, it transitioned to a dedicated product team in Seattle, who continued development from the foundation I handed over. I documented the end-to-end journey, the design rationale, and the product direction in enough detail that the incoming team could move forward without re-framing the problem or repeating the discovery I'd already done.
A first version of the platform launched in 2025, the first phase of a staged rollout. The work I designed, the assessment, the recommendations, the chatbot, was slated for a later phase, and I moved on to other projects before that phase went live. What I can speak to with confidence is the handover: the thinking held up well enough to be carried forward without anyone needing to start over.
The signal I trust most isn't the funding decision. It's that the Seattle team never had to reach out and ask what a decision meant, they built past where I left off without reopening discovery I had already done.
What I took away
I came into this project thinking my job was to design a platform. I left realising my job was to make a complex problem legible enough that other people could act on it, and the screens themselves were almost incidental to that.
What I was really building was shared understanding, of the problem, the constraints, and what was worth doing first. The handover is what proved that to me: understanding either transfers cleanly to someone else or it doesn't, and there's no partial credit for having it only in your own head.
Working without a dedicated team also meant holding the strategic and the tactical at the same time. The researcher was doing double duty as the de facto PM, which meant there was no separate product function to hand decisions off to, no design lead to check my reasoning against. Knowing when to go deep on craft and when to step back and question the brief is something I had to figure out without anyone else's judgment to lean on. That's the skill I keep reaching for.
Designing for engagement
Much of the training was mandatory, so the platform had to compete with low motivation. To address this, the researcher and I introduced:
🔹 An expertise score that moved as a learner progressed
🔹 A streak that rewarded consistency rather than one-off effort
🔹 A sense of where someone stood relative to their peers
These elements sat on top of the core product logic, making the experience feel rewarding and worth engaging with.
