Design Process

Leveraged design thinking process to analyze the AI/ML space, empathizing with data modelers, data scientists and engineers on their JTBD and the required UX solution

Problem Statement

This project was started as part of a larger Enterprise wide initiative to standardize the AI/ML process and governance. Defined problem space and context in a pitch deck as a HMW and outlined the projected goal

Project Brief

Worked with cross-functional partners to create project brief, outlining RACI chart, delivery phases, milestones, dependencies and the problem statement. This allowed for alignment and sign-off of objectives

Moderator Guide

Collaborated with research to build a moderator guide to understand user sentiment. Questions included identifying the user role, current state, use cases, opportunities and troubleshooting experiences

Building an Scalable Framework

In this stage of research, I took user quotes and sentiments and translated/mapped them to the features and solutions required to build. These were foundational to the OOUX approach, which included framing content as data, calls to actions, core content and objects

Deconstructing insights into the OOUX framework

Metadata

Defines attributes and properties of objects, offering contextual details to users for better understanding, such as a job’s metadata

Objects

The core components in the system that users interact with. Objects represent tangible or conceptual items, such as a “Job”

Calls to Action

CTAs are linked to objects and influence user decisions, such as restart a job, create an alert, view troubleshooting logs etc.

Core Content

What users come to the platform for, such as job status, completion time, or user historical runs and trends

Existing Requirements

To gather requirements, I looked at existing bespoke solutions across platforms and mapped the metadata, actions and CTAs to make a gap analysis in comparison to our research findings

OOUX Model Framing

Built a OOUX mapping, where the information architecture is mapped to user actions and data points uncovered during interviews. Notes in from the research are tied into the various data elements.

The research report also included the framework and construction of the OOUX model to help document understanding in a clear way with cross functional partners

Site Architecture

Leveraged information from OOUX exercise to build a site architecture and connections between data elements. This helped the tech team begin working on building the necessary components

The second version focused on answering some of the questions from engineers; such as the relationship between jobs running on different back-end instances and the associated metadata

Driving Conversations

The low fidelity design was quickly mocked up in Lucidspark, and drove conversations with data scientists and modelers as to different features, such as the starting dashboard, which data elements should be included in a single job run and how job rules would be constructed

Defining Opportunities

This also provided to an opportunity to test future ideas such as a “Profile” section and “Trend and History” which would provide users the ability to configure specific monitoring alerts and see data most pertinent for reporting and regulatory needs

High Fidelity Design

Translated the low fidelity flows and OOUX framework into high fidelity screens, aligning the dashboard, job details, alerts, and notification patterns with the existing MLX design system.

Engineering Handoff

Partnered with engineers to walk through the site architecture and rules component, clarifying how job metadata, thresholds, and notifications map to the underlying platform services and back-end instances.

QA & Release

Supported QA validation of the finalized execution monitoring flows ahead of release, confirming job states, alerts, and error handling matched the documented rules and thresholds.