Cutting edge system for data science and machine learning

Faculty Platform - formerly SherlockML, from ASI Data Science - is a tool built by data scientists for data scientists, which lets you explore your data, run experiments, and develop and deploy models all in one place.

faculty-intro

My Role

I joined Faculty as their first in-house designer. From the company branding (while still ASI Data Science) to the launch of their platform, I took the work from ground zero to delivery.
____

Toolbox

Sketch • Invision • AfterEffects • React

The Challenge

The main challenge consisted on diving into the data science world and being able to understand how they work, which tools are important to them and the struggles they face in their day to day lives.

When I joined, the platform was still only a Python library that Data Scientists could use in their notebooks, meaning no visual interface existed.

Working alongside the CTO and the team, I did whiteboarding and sketching excercises in order to create the basic framework... and that's how SherlockML was born.

faculty-whiteboard

User Flows

Faculty offers a data science fellowship, which allowed us to have data scientists coming to the office every couple months. We used these people as our testers and as basis for research on each feature, and built roadmaps upon results.

faculty-flow

Prototyping & Wireframing

Every new featured was mocked up either on wireframes or using the design library and tested both internally and with the data science fellows. Also, part of my work was polishing designs directly on React code, as well as prototyping some ideas on a staging environment before rolling out to clients.

faculty-wireframes
sml-craft
sml-components

Results & Learnings

There was a need to balance flexibility with ease of use. Our persona was very technical, which means they were able to create their setup the way they wanted, but that didn’t mean we shouldn’t eliminate IT burdens.

That way, Faculty Platform was designed delivering a slick experience to complex (and often painful) tasks that data scientists need to deal with in a daily basis.

sml-1
sml-2
sml-3
sml-4
See also

Discovery, design and prototyping for stats-driven sports streaming platform.

Design of AP's Turnaround & Restructuring Services fintech platform.

Heuristic evaluation and re-design of second-screen experience for football fans.

Work inquiries

hi[at]jonvieira.com

Or find me on LinkedInTwitter, Instagram and Dribbble.