Visualizing complex AI data for optimal insights

My Role

As a UX designer working directly with the CEO, CTO, and head of design, I joined the team when the product had an MVP demo and had just finished its first beta testing round. The CEO provided key user feedback, which guided my main tasks of optimizing the MVP demo and enhancing data visualization.


Duration

6 months (2023.9 - 2024.2)


Team

CTO: Talel Lachheb

CEO: Seng Tang

Head of Design: Earl Friedberg

Business Requirement

Optimizing an MLOps platform to lower learning curve and improve accessibility

ML Manager is Daesys's MLOps platform, designed as a B2B AI data monitoring solution. However, the demo has struggled to attract clients and secure partnerships.

Based on client feedback, two primary reasons have emerged as barriers to their willingness to adopt ML Manager:

UX Audit

MVP Demo Covered Main Use Flow, Lacked Accessibility and Learnability

I spent a week conducting a UX audit, summarizing tasks into a brief design handoff for the CEO and Head of Design.


Four key issues were identified that reduced the demo's accessibility and learnability:

Priority Assessment

Four Tasks Prioritization: 2 > 3 > 1 > 4

After meeting with the CEO and Head of Design to discuss the design brief, we aligned our priorities based on severity, ease of implementation, and feedback from beta testing.

How I solve task 2

Designed a non-linear wizard at the beginning

I designed a linear wizard with 5 steps to guide users in creating new tasks, including New APIs, Jobs, and Diagnostics. It is a standard solution that does not require users to learn the cost.

Test & Research

Conversations with target users and dev team

The standard answer doesn't always mean the right one. I did rapid testing with data analysts and got some constructive feedback:

With this insight, I recapped the discussion with the CEO, CTO, and Head of Design to categorize use cases when users create new tasks. I need to learn more about the workflow and necessary information

Revision

Streamlined into 3 steps; Designed progressive disclosure wizard

For creating APIs, Jobs, and Diagnostics, I reorganized the steps based on user actions rather than required information.

For more complex tasks like creating models, datatables, databases, and ML engines, I designed a progressive disclosure wizard—a more optimized solution than a linear wizard to address personalized or specific management needs.

How I solve task 3

Designed an overview dashboard for quick model diagnostics

I extracted model diagnostic information from the raw text format in version 1.0, allowing users to browse key diagnostics through the dashboard quickly. It eliminates the need to click through individual models or rely on email notifications, providing a more efficient way to monitor model performance at a glance.

Quick Fixes

UX Layout Enhancements

To enhance navigation and resolve layout issues, I restructured the 4-level menu into a 3-level navigation, improving the flow of tasks 1 and 4. Additionally, I implemented a slide-in drawer design to address the overlapping area issue, ensuring a more seamless user experience.


3-level Nav Solution :

Slide-in Drawer Solution :

Slide-in Drawer Solution :

Slide-in Drawer Solution :

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Get in touch

Let's connect on LinkedIn or have coffee chats to explore AI and interactive design together!☕️

Get in touch

Let's connect on LinkedIn or have coffee chats to explore AI and interactive design together!☕️

Get in touch

Let's connect on LinkedIn or have coffee chats to explore AI and interactive design together!☕️