My talented team and I are in the eye of the GenAI storm, using all of our skills to make Amazon Bedrock the best choice for developers and enterprises to tap into Generative Artificial Intelligence.

I managed user experience design for a range of AI/ML products and services, including: AWS Panorama (edge appliances and applications enabling computer vision for off-the-shelf IP cameras), Amazon Lookout for Metrics (machine learning anomaly detection for business and operational data), Amazon Forecast (machine learning forecasting for time-series business data), Amazon Personalize (the same machine learning personalization and recommendation system used by amazon.com), AWS DeepLabs (home of DeepLens, DeepComposer, DeepRacer), Amazon Comprehend Medical (automated machine learning text extraction from unstructured medical information), Amazon HealthLake (secure storage, analysis, and query of health data using natural language processing), AWS RoboMaker (run, scale, and automate robotics simulations), AWS IoT RoboRunner (vendor independent robotics application development and fleet management), and more in the works.

At the intersection of IoT Sensor networks, ML based image recognition, and Edge computing, I lead a small team of designers working to bring an innovative new AI powered device that enables the creation of custom on-premises computer vision applications by customers using their existing off-the-shelf IP network cameras.

As part of the AWS Database and Analytics team I was the sole designer responsible for AWS Glue and AWS Lake Formation.

Working closely with product management and engineering to understand the customers, business goals, timeline and priorities, and technology capabilities, I delivered user research, requirements analysis, design ideation, concept wireframes, interactive prototypes, detailed design assets, and customer validation on an aggressive schedule.

During this period we introduced over 30 new features in Glue, added 15 additional regions of availability, and built an entirely new service in parallel with Lake Formation.

Significant Glue features introduced included: support for DynamoDB tables as a data source, Amazon SageMaker notebooks for development endpoints, resource tagging, Workflows for ETL orchestration, job bookmarks for Parquet and ORC files, FindMatches ML transforms, custom SSL certificates for JDBC connections, rewind job bookmarks, and custom JDBC drivers.

As a new service, AWS Lake Formation builds upon the Glue data catalog, workflow orchestration, and ETL job infrastructure, creating Blueprints that simplify and automate data lake ingestion. It extends the data catalog with user defined metadata, allowing for rich data discovery by end-users. And it adds a powerful access control system where permissions are granted and revoked for users, groups, and roles to data at the table, column, or row level. All data lake activity is logged for compliance and auditing purposes.