AI Model Training Platform

Transcription

1 Amazon AWS for AI Model Management, Training, and Testing ROLE: Lead UX Designer DELIVERABLES: System Sitemap Wireframes Mockups

2 VISION TEAM To make AI more accessible to the masses by creating a platform that allows data companies, data scientists, and developers to come together to use AI. GOAL - FIRST STAGE 2 x Product management 2 x Design 6 x Development & QA Tackling one core component of the AI platform: data tagging & management Ability to clean and prepare large amount of data for AI model training TIME FRAME This is an ongoing project, I helped in leading the product and design CHALLENGE Large scope with a short timeline to validate idea New to the field of AI for the first 6 months of the product, solidifying the product vision and strategy. First of its kind product with multiple stakeholders with varying goals

3 JULIE SUN DESIGN PROCESS 1. Understanding project scope and aligning with product vision For this project I was involved in the strategizing, planning, and architecturing of the platform as well as leading the UX design. The initial stage I led product meetings and used the Object Oriented UX (OOUX) strategy to break up the product into series of objects, intent, and actions, making it easy to visualize the basic structure and navigation of a product. 2. Know thy product Since the field of AI is a new field for myself and many of the team members, we had to learn as much about AI, deep learning, and model training as we could from our data scientists and online sources. Learning about convolutinal neural networks and model training really helped to understand the product beyond the surface interaction level.

4 PROCESS 3. Prioritizing We broke the platform down into 4 epics: Data Tagging, Data Management, Model Training, and Model Testing. We decided to tackle Data tagging first as there are no big players in this field and it would also bring immediate value to our data scientists who are currently working with AI models for other projects and are having a challenging time gathering quality data to train their models. 4. Breaking down epics, creating user stories, and a features road map Working with the product manager and dev manager in writing up user stories to ensure we covered all cases and that the interactions are fluid and consistent and easy to navigate around. We prioritized features and groomed the development backlog on weekly basis to stay on top of the agile development process with the team. TIMELINE Q1 Q2 Q3 DATA TAGGING DATA MANAGMENT MODEL TRAINING MODEL TESTING

5 PROCESS 5. Creating wireframes, mockups and rapid prototypes Created wireframes for platform on high level based on the sitemap used for features road map then mocked up high fidelity UI and screens for sign off, review, and development handoff 6. Testing, gathering feedback and making continuous improvements To test our data tagging tool, we got 50+ volunteers in the company to spend min 20 minutes using the platform while simultaneously help tag the large amount of data that the data scientists need to perfect their models. In the end we were able to not only get insight on how the tool can be improved but also quality data that can help our data scientists out with their projects.

6 DELIVERED System map, UI, and UX component of a web application platform that allows data tagging model management, training, and testing.