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



DURATION
DURATION
3 Months
3 Months
CLIENT
CLIENT
AIMMO
AIMMO
ROLE
ROLE
Lead Product Designer
Lead Product Designer
YEAR
YEAR
2023
2023
About Company
About Company
AIMMO: AI Data Solutions
AIMMO: AI Data Solutions
Industry
Industry
AI/Data Solution
AI/Data Solution
Headquarters
Headquarters
Seongnam, South Korea
Seongnam, South Korea
Company size
Company size
201-500+
201-500+
AIMMO provides advanced AI-driven data solutions across industries such as autonomous driving, smart cities, robotics, drones, and construction. By combining human expertise with AI, AIMMO enhances efficiency, accelerates innovation, and delivers real-world applications that optimize technology use at scale.
AIMMO provides advanced AI-driven data solutions across industries such as autonomous driving, smart cities, robotics, drones, and construction. By combining human expertise with AI, AIMMO enhances efficiency, accelerates innovation, and delivers real-world applications that optimize technology use at scale.
Top Achievements
Top Achievements
Market Innovation
Market Innovation
3
3
Months
Months
to deliver to 0 → 1 MVP
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
Operational Efficiency
+60
+60
%
%
Internal User Satisfaction
Internal User Satisfaction
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 validated 60% boost in internal user satisfaction.
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 validated 60% boost in internal user satisfaction.



Background
Background
Why a core revamp was essential?
Challenge: From Annotation Tool to All-in-One Model Deployment Platform
Challenge: From Annotation Tool to All-in-One Model Deployment Platform
• AIMMO had a working annotation tool but no full end-to-end platform
• Market research showed no all-in-one solutions existed, despite customer demand
• Collaborated with the business team to validate insights through interviews
• Tasked with designing a prototype version under a tight CES deadline for demonstration
• AIMMO had a working annotation tool but no full end-to-end platform
• Market research showed no all-in-one solutions existed, despite customer demand
• Collaborated with the business team to validate insights through interviews
• Tasked with designing a prototype version under a tight CES deadline for demonstration


Company focused exclusively on data annotation.
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
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.
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
UX Research
Research & Discovery: Phase 1
Research & Discovery: Phase 1
Company Seeks Comprehensive All-in-One AI Model Deployment Service
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.
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?”
“Essential Elements for Upcoming Contracts?”
MITSUBISHI
Emphasized that uploading large files for driver training increases operational load due to scalability challenges.
Emphasized that uploading large files for driver training increases operational load due to scalability challenges.
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.
It was highlighted that server scalability directly increases operational burden.
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.
Emphasized that uploading large files for driver training increases operational load due to scalability challenges.
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.
CBM Data: No automation of logistics volume-related measurements.
CBM Data: No automation of logistics volume-related measurements.
Research & Discovery: Phase 2
Research & Discovery: Phase 2
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?
We conducted field interviews with 20 internal users to identify UX issues, leading us to initiate significant UX improvements.
We conducted field interviews with 20 internal users to identify UX issues, leading us to initiate significant UX improvements.



Interview insight:
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"
"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"
"I wish the sidebar would collapse or shrink when working with lots of content"
Research & Discovery: Phase 3
Research & Discovery: Phase 3
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.
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.
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
Research & Discovery: Phase 4
Reviewed the end-to-end flow of Model deployment
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.
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
Solutions
Comprehensive AI Training Product Redesign
Comprehensive AI Training Product Redesign
Solution 1
Solution 1
Enhanced Labeling System
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.
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

Frequent failed GT uploads and ambiguous data quantities were difficult to identify.
Data uploads are critical to project progress.
Interviewed internal labelers to prioritize and address the most significant UX issues first.
If the failure reason is long, it should be downloadable.
For optimal user interaction, the "Retry all" button should have low affordance.
BEFORE
AFTER

Frequent failed GT uploads and ambiguous data quantities were difficult to identify.
Data uploads are critical to project progress.
Interviewed internal labelers to prioritize and address the most significant UX issues first.
If the failure reason is long, it should be downloadable.
For optimal user interaction, the "Retry all" button should have low affordance.
BEFORE
AFTER

Frequent failed GT uploads and ambiguous data quantities were difficult to identify.
Data uploads are critical to project progress.
Interviewed internal labelers to prioritize and address the most significant UX issues first.
If the failure reason is long, it should be downloadable.
For optimal user interaction, the "Retry all" button should have low affordance.
Solution 2
Solution 2
Visualizing the AI Data Curation Process
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.
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
Solution 3
Synthetic Data Integration for Superior Curation
Synthetic Data Integration for Superior Curation
Leveraged synthetic data to improve data quality and bolster the curation process.
Leveraged synthetic data to improve data quality and bolster the curation process.



Key Features
1.
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.
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.
2.
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.
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.
3.
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.
The design ensures that users can navigate between original and generated images effortlessly, making it simple to mange and curate large datasets.
Solution 4
Solution 4
Model Deployment Workflow Creation
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.
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
Comprehensive Model Setup: From Dataset Configuration to Augmentation



Augmentation Step in Model Workflow
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.
The augmentation settings are seamlessly integrated into the model creation workflow, allowing users to easily apply these transformations without disrupting the flow.
Team Decision
Team Decision
My team and I decided to incorporate this augmentation step to generate richer datasets, ensuring the model is trained on a larger and more diverse set of data. This enhancement is crucial for producing models that perform well in varied real-world scenarios.
My team and I decided to incorporate this augmentation step to generate richer datasets, ensuring the model is trained on a larger and more diverse set of data. This enhancement is crucial for producing models that perform well in varied real-world scenarios.
Impact
Impact
Incorporating augmentation at this stage enhances the dataset by introducing controlled variability, leading to a more resilient and well-rounded AI model. this step is vital for creating high-performance models capable of handing diverse real-world scenarios.
Incorporating augmentation at this stage enhances the dataset by introducing controlled variability, leading to a more resilient and well-rounded AI model. this step is vital for creating high-performance models capable of handing diverse real-world scenarios.
Solution 5
Solution 5
Model Training: Post-Training Analysis and Evaluation
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.
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
Graph Analysis
User can access detailed graphs that depict the model’s performance over the training period.
User can access detailed graphs that depict the model’s performance over the training period.



Evaluation Section
Evaluation Section
The evaluation section provides a visual comparison between the Ground Truth and the Expected Checkpoint.
The evaluation section provides a visual comparison between the Ground Truth and the Expected Checkpoint.
Takeaways
Takeaways
Takeaway 1
Takeaway 1
Focus on High-Impact MVPs Under Tight Deadlines
Focus on High-Impact MVPs Under Tight Deadlines
"Never underestimate the power of a tight deadline."
"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.
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
Study with AI Labs team for Meta Filter Feature
2. Treat Internal Teams as Users
2. Treat Internal Teams as Users
"Internal Users are Users, Too."
"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.
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.
Visualizing complex screen flows to align Development and QA teams.
3. Innovation Value Award: Design Operations
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 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.
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.






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