Paul Armstrong

Learn to architect self-evolving systems using agentic AI that align with AWS Well-Architected principles. Explore patterns for implementing autonomous monitoring and self-healing capabilities with Amazon Bedrock Agents.

Many airlines want to adapt pricing in real time to match market conditions, making pricing models more complex. In this chalk talk, learn how you can use machine learning to drive increased margins with the appropriate risk mitigation and customer experience controls.

In this chalk talk, learn how to use the data mesh methodology to build a data lake/lake house architecture and an operational data store for customer and operations data with the ability to separate compute and storage and implement schema-on-read. Discover how to do so using NoSQL databases and implementing open standards like Parquet, JSON, and Avro to meet the needs of the agile enterprise.

Amazon SageMaker is a fully managed service that removes the heavy lifting from each step of the machine learning (ML) workflow and provides every developer and data scientist with the ability to build, train, and deploy ML models quickly. In this interactive workshop, we work on the different aspects of the ML workflow to build, train, and deploy a model using all the capabilities of Amazon...

Industry 4.0 demands greater insight into data to bring people, processes, and equipment together. In this workshop, we illustrate how to gain business insights from video and voice data sources, highlighting the data pipeline.