0ml.ai platform

Designing 0ML.ai — A Scalable Data Annotation Platform

Material & Design — AL ML Platform

Overview — SaaS

In Year of 2020, Application is received National award for providing AI based solution to Indian government for intelligent E-Governance.

0ml.ai machine learning models are trained to leverage your data and can be easily integrated with any application using Rest APIs. We use state-of-the-art infrastructure, which scales intelligently according to the use cases.

My role as the Product Designer was to create an intuitive, collaborative, and efficient platform for large-scale data annotation while ensuring an enjoyable user experience in a traditionally painstaking process.

The App: how it works and what it does do ?

Data Annotation Overview

Text Annotation

Audio Annotation

Image Annotation

Video Annotation

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing business. A new generation of powerful products and services are being developed to solve old problems in new ways and unlock previously impossible opportunities to improve the way we work. By shaking up the status quo new opportunities that were only imagined previously have emerged.

One company, in particular, is at the forefront of the AI and ML revolution. They are providing a scalable, secure software platform that enables product developers, scientists, and data managers to accelerate the research and development of new and custom material applications. Comparative analysis, iterative sandbox evaluation, and predictive modeling propel user initiatives forward. One key to unlocking the full potential of such a powerful platform is to provide an intuitive interface that maximizes the potential of the AI and machine learning models.

Train and test any model using AI ML

The problem

How to annotate data fast and produce high-quality data annotation on a large scale?

There are existing tools that offer similar to 0ml.ai. But none of them provides the base of a scalable annotation process promptly, and those are not having advanced training detection models like as listed below.

A very talented group of engineers (my team) and I, the product designer, took the initiative to design an internal annotation platform, which will allow :

• image classification
• text classification
• sentiment analysis
• object detection
• chatbot
• face detection
• structure data modeling
• geographic information system

How do I fix this? — Brainstorming

Solution

To address these challenges, I proposed and implemented a Design Patterns and UI Library. This modular approach ensured that the platform was:

• Simple: A clean, minimal UI for seamless navigation.
• Reliable: Tools to reduce errors and automate repetitive tasks.
• Scalable: Designed for collaboration with multiple annotators working on shared datasets.

I began with a robust design system, using the Atomic Design Methodology, which enabled flexibility and reusability across the platform.

0ML blueprint

Data labeling and task process in the platform

Data labeling required that we first craft the data text annotation tool.

Design Process

Research & Inspiration: Used leading systems — Material Design and Ant Design System as references for component-rich frameworks.

Implementation

1. Atomic Design Methodology:

• Foundations: Grid layouts, modular typography scales, and consistent color palettes.
• Components: Buttons, inputs, molecules, organisms, and page templates.
• Collaborative Tools: Developed in Adobe XD and Storybook for seamless team collaboration and iterative refinement.

Core Features:

• Custom Models: Users can train data models for specific use cases, e.g., vehicle number plate recognition.
• Automated Operations: APIs enable automatic actions for repetitive tasks.
• Collaborative Annotation: Multiple annotators can work simultaneously on a dataset, enhancing efficiency.

Key Features

• Codeless ML Development: Simplified tools for users to build models without extensive coding expertise.
• 24/7 Customer Support: Assistance for deployment and troubleshooting.
• Scalable Infrastructure: Capable of handling large-scale annotation projects.
• Diverse Annotation Types: Text, audio, image, video, and custom use cases supported.

Results

The platform successfully enabled teams to train data models with high efficiency and minimal frustration. By balancing priorities, making thoughtful design trade-offs, and fostering team collaboration, we created a tool that made the annotation process faster, more reliable, and enjoyable.

Team Manager, Business analyst, and Developers successfully enabled modeling, data analysis, and material design space exploration. All overlaid with recommendations and predictors of success. In the end, we are empowering users to identify new opportunities and bring new innovations into the world.

before image upload or link not pasted in task process/method

after auto objection detection task process/method

Reflections & Learnings

The design system remains a work in progress, evolving with new insights and user feedback. This experience taught me how to:

• Prioritize goals in complex projects.
• Foster team involvement and consensus.
• Balance trade-offs to deliver both functionality and aesthetics.

Through 0ML.ai, we delivered a user-centric solution that enhanced productivity while supporting advanced machine learning applications.

This project exemplifies my ability to design scalable, collaborative platforms that meet user needs while pushing the boundaries of innovation in the AI and ML space.