Learn how UI improvement for Amazon SageMaker, a fully managed machine learning service, led to improved user adaption.
Challenges
- Poor user experience and lack of interactive UI for Amazon SageMaker users led to loss of business
- Difficult to execute comprehensive feature testing for the application leading to poor quality features and unhappy end customers
Solutions
- Integrated individual technologies for seamless workflow of the application
- Enhanced overall architecture of the code base with respect to how Jupyter lab, JEST and Cypress are integrated with React
- Styled the framework using Emotion as a CSS framework
- Created Jest automation scripts for validating various features
- Built Cypress automation test cases on shadowDOM structure (which is a component-based architecture)
Outcomes
Increased User Adoption
Due to an intuitive UI and improved user experience
Accelerated Feature Expansion
with new import related features for Athena and Redshift
Faster deployment
with UI Automation executed on new deployments for rapid sanity testing
