2026 IEEE International Conference on AI and Data Analytics
(ICAD 2026)
June 11 – 12, 2026
Boston, Massachusetts
Workshop
Hands-On Generative AI: Building Applications with Amazon Bedrock and SageMaker Studio
Foundation models have made generative AI widely accessible, yet a practical gap remains between experimenting with a model and building a working application. This workshop explores how managed cloud services — specifically Amazon Bedrock and Amazon SageMaker Studio — can bridge that gap. Through live demonstrations and guided coding exercises, we show how practitioners can select and interact with foundation models from multiple providers, apply prompt engineering techniques, ground model responses in domain-specific data, and implement responsible AI guardrails. We also discuss how researchers who train custom models can import them into managed environments to leverage the same application infrastructure. The workshop assumes working Python knowledge and familiarity with AI/ML concepts; no prior cloud experience is required.
Instructors:
Brian McCarthy, Senior Solutions Architect, AWS
Aditya Singh, AI/ML Specialist Solutions Architect , AWS
Bios:
Brian McCarthy is a Senior Solutions Architect at AWS, specializing in research computing and AI/ML. He partners with academic and research institutions to design cloud architectures for high-performance computing, machine learning, and data-intensive workloads. McCarthy has presented at AWS re:Invent and regional technology conferences and has published on topics including HPC and resilient cloud architectures. He holds a Bachelor of Science in Chemical Engineering from the University of Massachusetts Amherst.
- Aditya Singh is an AI/ML Specialist Solutions Architect at AWS, where he helps higher education and state/local government organizations adopt generative AI and machine learning platforms. Prior to AWS, he developed large-scale AI systems in financial services. Aditya has served as an instructor at George Mason University and contributed as a technical reviewer for the SciPy Conference. He holds a Master’s degree in Computer Science with a Machine Learning specialization from George Mason University.
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