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Revamping the AI Model Development Life Cycle

AIMMO End-to-End Solution Tool Redesign

Demo Link

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Demo Link

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Solution

Overview

PROJECT DURATION

3 Months

CLIENT

AIMMO

ROLE

Product Designer
(Lead on UX)

YEAR

2023

INDUSTRY

B2B SaaS · AI Data Operations Platform

MY ROLE & RESPONSIBILITIES

UX strategy, end-to-end flow architecture, interaction design, extension of the existing design system, and high-fidelity prototyping for VIP client demos.

WORKED WITH

Product Designer (1),

Product Owners (2), QA (1)
Front-end Engineer (1),

Back-end Engineer (1)
AI Labs (1), MLOps (1), Business Team (2)

USERS

• VIP External Clients

• Internal Operators

PRESS

↗ Korean Herald

↗ Canadian Auto Dealer

↗ PR Newswire

GOAL

Simplify complex AI model deployment workflows and improve internal adoption while enabling reliable demonstrations for enterprise clients ahead of CES.

CONSTRAINTS

Legacy platform architecture, tight CES launch timeline, and coordination across multiple technical and business stakeholders.

RESULTS

• Supported enterprise partnership initiatives including collaboration with Mitsubishi Electric.

• Reduced workflow confusion and handoffs between internal teams.

• Enabled smoother CES demos for VIP clients.

• Improved VIP client experience during demos, strengthening external stakeholder trust.

• Set foundation for future process standardization and potential external rollout.

💡Quantitative usability metrics were not formally tracked; impact was assessed through observed usage, internal feedback, and demo readiness.

Top Achievements

Market Innovation

3 Months

to deliver to 0 → 1 MVP

Delivered the V1 End-to-End platform in time for the critical CES Global Launch, enabling the sales team to demo to VIP stakeholders.

• Met a critical 3-month deadline to secure a successful demonstration for VIPs at the CES launch

• Established a foundation for a new, unified product line, shifting the company's focus from siloed annotation tools to a comprehensive deployment platform.

Delivered the V1 End-to-End platform in time for the critical CES Global Launch, enabling the sales team to demo to VIP stakeholders.

• Met a critical 3-month deadline to secure a successful demonstration for VIPs at the CES launch

• Established a foundation for a new, unified product line, shifting the company's focus from siloed annotation tools to a comprehensive deployment platform.

Operational Efficiency

+60

%

Internal User Satisfaction (based on internal Business Intelligence)

Systematically resolved critical UX debt across core labeling and model training workflows. Implemented efficiency-focused UI improvements (collapsible sidebar, clearer error states) which drastically reduced task completion time, leading to a 60% boost in user satisfaction based on internal Business Intelligence (BI) performance tracking.

Background

Why this mattered to the company?

Challenge: From Annotation Tool to All-in-One Model Deployment Platform

• This project supported AIMMO's ability to demonstrate scalable AI deployment workflows to enterprise clients under a high-visibility CES deadline

• Market research showed no all-in-one solutions existed, despite customer demand

• Collaborated with the business team to validate insights through interviews

Company focused exclusively on data annotation.

The goal is to enhance the product lines to offer comprehensive end-to-end model training and deployment.

Objective

The main objective was to streamline processes, minimizing labor intensity and boosting user engagement, in line with the company's Q4 goals for operational optimization.

UX Research

Research & Discovery: Phase 1

Company Seeks Comprehensive All-in-One AI Model Deployment Service

Before we started the redesign, we surveyed our customers and worked with the buisness team to collect customer testimonials. The company's input was essential in finalizing the next year deal.

“Essential Elements for Upcoming Contracts?”

MITSUBISHI

Emphasized that uploading large files for driver training increases operational load due to scalability challenges.

MAGNA

It was highlighted that server scalability directly increases operational burden.

PIXELPLUS

Emphasized that uploading large files for driver training increases operational load due to scalability challenges.

OUR HOME (CBM)

CBM Data: No automation of logistics volume-related measurements.

Research & Discovery: Phase 2

How can we boost productivity for users of our annotation tools, both internal and external?

We conducted field interviews with 20 internal users to identify UX issues, leading us to initiate significant UX improvements.

Interview insight:

65%

of Group A users

Admins & Project Managers

"It's hard to scan project progress, everything's buried in a long, scrollable list"

Interview insight:

80%

of Group B users

Labeling Workers

"I wish the sidebar would collapse or shrink when working with lots of content"

Research & Discovery: Phase 3

To design the tool, I sought feedback from AI developers and the MLOps team.

The development team and I met at least twice a week to design the product, as this is a tool for AI developers. To effectively tackle the project, I created a research plan to identify and address knowledge gaps, enabling informed design decisions. Understanding the AI training tool issues and evaluating competitors' offerings were crucial for the successful redesign of the product.

Raw file to model deployment simple version of information architecture

Raw Files

(Data from Autonomous Vehicles)

Dataset List

Data curation for

high-quality model training

Using artificial data

(Synthetic data)

to improve the training of models

Model

Workflow > Training > Deployment

Research & Discovery: Phase 4

Reviewed the end-to-end flow of Model deployment

I reviewed the end-to-end flow to gain a better understanding of the model deployment process.
I created the flowchart to identify which touch points caused pain points and to model the deployment flow from scratch.

Solutions

Solution 1

Enhanced Labeling System

Improved the labeling process by integrating an automated failure history log. Users can now download detailed Excel or CSV reports of any upload failures, facilitating quick issue resolution and ensuring data integrity.

BEFORE

AFTER

The Problem (Legacy Modal)

The original failure history was a major bottleneck for our labeling team:

✓ Visibility Issues:
Internal users couldn't distinguish between critical GT upload failures and minor data mismatches.

✓ Dead Ends:
Error messages were often truncated, forcing users to ask engineers for backend logs to understand a simple failure.

✓ Low Affordability:
The "Retry All" button was too prominent, leading to accidental clicks that caused file corruption.

BEFORE

AFTER

The Problem (Legacy Modal)

The original failure history was a major bottleneck for our labeling team:

✓ Visibility Issues:
Internal users couldn't distinguish between critical GT upload failures and minor data mismatches.

✓ Dead Ends:
Error messages were often truncated, forcing users to ask engineers for backend logs to understand a simple failure.

✓ Low Affordability:
The "Retry All" button was too prominent, leading to accidental clicks that caused file corruption.

Solution 2

Visualizing the AI Data Curation Process

Embedding visualizations offer deeper insights into model performance and data quality beyond traditional metrics. This is why our team focused on developing these visualizations.

Drag the cursor to select and display the thumbnail images.

Solution 3

Synthetic Data Integration for Superior Curation

Leveraged synthetic data to improve data quality and bolster the curation process.

Key Features

Meta Filter Options

Designed users to adjust the meta values (e.g., time of day) for the generated images, providing control over the specific characteristics of the synthetic data.

Advanced Settings

Offers the ability to choose different models (e.g., Stable Diffusion V1.0) and customize the number of images generated, giving users flexibility in the data creation process.

User-Friendly Interface

The design ensures that users can navigate between original and generated images effortlessly, making it simple to mange and curate large datasets.

Solution 4

Model Deployment Workflow Creation

I worked with my team to develop the Model Deployment Workflow, a crucial part of the AI model development process. This workflow was built from scratch to guide users through deploying AI models efficiently and intuitively.

Comprehensive Model Setup: From Dataset Configuration to Augmentation

Augmentation Step in Model Workflow

User can select various augmentation options, such as flipping, rotation, adjustments, to enhance the variability in the dataset. This ensure that the model is trained on a wider range of data, improving its accuracy and generalization.

The augmentation settings are seamlessly integrated into the model creation workflow, allowing users to easily apply these transformations without disrupting the flow.

Strategic Decision

I added an "automatic data-generator" step (Augmentation) to the training flow. This ensures the AI learns from a massive variety of images, not just a few static examples.

Impact

Real-World Reliability:

By training on diverse data, the AI become much smarter at handling "messy" real-world situations.

Efficiency:

We automated the process of creating "richer" datasets, saving hours of manual work for the engineering team.

Solution 5

Model Training: Post-Training Analysis and Evaluation

With just three months to deliver an MVP for CES, I collaborated closely with the Labs and AI engineering teams to design the first phase of model deployment. Despite having no prior experience in model training or data curation, I quickly learned the essentials and focused on creating a clear, usable interface for post-training analysis.

Graph Analysis

User can access detailed graphs that depict the model’s performance over the training period.

Evaluation Section

The evaluation section provides a visual comparison between the Ground Truth and the Expected Checkpoint.

Takeaways

Takeaway 1

Focus on High-Impact MVPs Under Tight Deadlines

Never underestimate the power of a tight deadline.

With only three months until CES, I focused ruthlessly on prioritizing core MVP features that would deliver the most business value. I quickly adapted to complex AI/ML domain by collaborating daily with AI engineers to successfully ship a functional prototype on time.

Study with AI Labs team for Meta Filter Feature

Takeaway 2

Treat Internal Teams as Users

Internal Users are Users, Too.

I learned that great UX doesn't stop at the customer. By designing clearer screen flows and documentation for our developers and QA team, we ensured stronger alignment and smoother handoff, which directly boosted internal efficiency and team morale.

Visualizing complex screen flows to align Development and QA teams.

Takeaway 3

Innovation Value Award: Design Operations

"I realized that the 'product' isn't just what we ship to customers, it’s also the system our team uses to build it."

I spearheaded the implementation of new standards to eliminate cross-functional friction and improve our design practice. This included creating a Figma File Organization & Project Documentation Policy for the design team and establishing new developer handoff protocols. This systemic contribution earned the Design Operations Achievement Award 2023, providing that designing for internal alignment is key to scaling a product organization.

Press

Recommendation

Oct 01, 2025

I am delighted to recommend Jess O, with whom I had the pleasure of working over the past year. Jess consistently demonstrates a strong mastery of user-centered design principles and applies them thoughtfully in every project. She has an exceptional ability to empathize with end-users, uncover their needs and pain points, and translate those insights into intuitive, user-friendly interfaces. Her keen eye for detail and commitment to resolving issues efficiently make her stand out as a designer. What I admire most about Jess is her adaptability and her ability to excel in fast-paced, collaborative environments. She is not only an excellent team player but also communicates effectively with both designers and developers, bridging the gap between vision and execution seamlessly. I am confident that Jess will be an invaluable asset to any organization or project fortunate enough to have her.

Andy Joe - Product Owner @AIMMO

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Revamping the AI Model Development Life Cycle

Revamping the AI Model Development Life Cycle

Revamping the AI Model Development Life Cycle

AIMMO End-to-End Solution Tool Redesign

AIMMO End-to-End Solution Tool Redesign

AIMMO End-to-End Solution Tool Redesign

Demo Link

↗

Solution

Solution

Overview

Overview

PROJECT DURATION

3 Months

CLIENT

AIMMO

ROLE

Product Designer(Lead on UX)

YEAR

2023

PRESS

GOAL

CONSTRAINTS

RESULTS

Top Achievements

Top Achievements

Market Innovation

Market Innovation

3

3

Months

Months

to deliver to 0 → 1 MVP

to deliver to 0 → 1 MVP

Operational Efficiency

Operational Efficiency

+60

+60

%

%

Internal User Satisfaction (based on internal Business Intelligence)

Internal User Satisfaction (based on internal Business Intelligence)

Background

Background

Challenge: From Annotation Tool to All-in-One Model Deployment Platform

Challenge: From Annotation Tool to All-in-One Model Deployment Platform

UX Research

UX Research

Company Seeks Comprehensive All-in-One AI Model Deployment Service

Company Seeks Comprehensive All-in-One AI Model Deployment Service

“Essential Elements for Upcoming Contracts?”

“Essential Elements for Upcoming Contracts?”

How can we boost productivity for users of our annotation tools, both internal and external?

How can we boost productivity for users of our annotation tools, both internal and external?

To design the tool, I sought feedback from AI developers and the MLOps team.

To design the tool, I sought feedback from AI developers and the MLOps team.

Reviewed the end-to-end flow of Model deployment

Reviewed the end-to-end flow of Model deployment

Solutions

Solutions

Enhanced Labeling System

Enhanced Labeling System

Visualizing the AI Data Curation Process

Visualizing the AI Data Curation Process

Synthetic Data Integration for Superior Curation

Synthetic Data Integration for Superior Curation

Model Deployment Workflow Creation

Model Deployment Workflow Creation

Model Training: Post-Training Analysis and Evaluation

Model Training: Post-Training Analysis and Evaluation

Takeaways

Takeaways

Focus on High-Impact MVPs Under Tight Deadlines

Focus on High-Impact MVPs Under Tight Deadlines

Treat Internal Teams as Users

Treat Internal Teams as Users

Innovation Value Award: Design Operations

Innovation Value Award: Design Operations

Press

Press

Product Designer
(Lead on UX)