AutoML
Automate ML model workflow
Problem: Customers were most frustrated by sourcing high-quality images, limited automation and configuration, and lack of S3 bucket selection in model building.
Solution: Re-architected workflow by modernizing data pipelines, model training, and deployment processes. Implemented no/low code auto ML capabilities to democratize model development, enabling non-technical stakeholders to build predictive models without programming expertise while reducing time-to-market. Jump to demo
Impact: Translated advanced deep learning workflows into a no-code interface, reducing time-to-model-deployment by 40% for enterprise users across retail, media, and public sector verticals.
Process: Conducted user research with ML scientists and solution architects to understand technical pain points and workflow bottlenecks. Read more...
These insights directly informed the information architecture and design of a self-guided workflow—translating complex user requirements into intuitive design patterns including progressive disclosure for advanced configurations, contextual examples at decision points, and automated suggestions based on user goals, ultimately simplifying ML model building without sacrificing functionality. [hide]
