H2O Actions is a platform designed to help data scientists manage datasets, run Python code, and conduct AI experiments in one unified environment. The goal was to simplify workflows by reducing the need to switch between multiple tools and interfaces.
H2O Actions is a concept platform designed to streamline the workflow of data scientists by combining dataset management, code execution, and AI-powered assistance in one unified workspace. The project focuses on simplifying complex data processes through a clean and intuitive interface, allowing users to run experiments, manage data, and interact with AI tools more efficiently without switching between multiple platforms.
Data scientists often rely on multiple tools to manage datasets, write code, and run experiments, which can make workflows fragmented and inefficient. The challenge was to design a unified platform that simplifies these processes while keeping the interface intuitive and easy to navigate.
To create a streamlined workspace where data scientists can manage datasets, execute code, and interact with AI tools within a single platform. The goal was to improve efficiency, reduce tool-switching, and provide a clean, user-friendly interface for experimentation and data analysis.
Used the Design Thinking approach for solving this problem. To practice design thinking we followed the below process.
I worked with 3 data scientist in H2O to understand main pain points. We brainstorm the idea and created a document outlining key features and goals of the application. This gave me a brief idea about the application and it’s scope.
To dive into deep of our scope I tried many competitors like Databricks, Deepnote and prepared a document to brainstorm and to collaborate with data scientist to understand the scope.
In Ideate phase I worked on lo-fi wireframes and present and test it out with the data scientists I worked with. I have created couple of concept in this stage to test it out.
Once we had a clear vision and flow about how app needs to work I started working on UI design.
I have created local design system based on components that were unique to this application. All the other components are from out in house design system.
Once we created an MVP version of the prototype I have conducted usability test to identify weather this application is usable for the data scientists. Â
