Amazon Web Services (AWS): Machine Learning in the Cloud with Sagemaker

Sep 28, 2023, 11:00 am3:00 pm
View location on My PrincetonU
Princeton students, graduate students, researchers, faculty, and staff


Event Description
This workshop will introduce participants to cloud computing via Amazon Web Services and provide them with hands-on machine learning experience using Sagemaker.


11:00 - 11:30: Session I - Theory
11:30 - 12:30: Session II - Hands-On Lab
12:30 - 01:00: Lunch will be provided
01:00 - 01:45: Session III - Theory
01:45 - 03:00: Session IV - Hands-On Lab

Participants should bring a laptop with an up-to-date web browser and a power cable.

Meet the Instructors:

Shannon Smith is a senior account manager for AWS, specializing in supporting R1 universities and academic medical centers in the NJ/DE/PA region. With over two years of experience at AWS, she is a trusted advisor, providing tailored cloud computing solutions to meet the unique needs of her higher education customers. Shannon's passion for technology, has allowed her to asset in driving innovation and success for the institutions she serves.

Abhilash Thallapally is a Solutions Architect at AWS helping public sector customers design and build scalable AI/ML solutions using Amazon SageMaker. His work covers a wide range of ML use cases, with a primary interest in computer vision, Generative AI, IoT, and deploying cost optimized solutions on AWS.

Mamta Vaidya is a Solutions Architecture Manager at Amazon Web Services(AWS) accelerating customers in their adoption to the cloud in the area of bigdata analytics and foundational architecture. She has over 20 years of experience in architecting and building enterprise systems in higher education, government, healthcare and finance with strong management skills. Her team specializes in building proof of concepts to enable and accelerate cloud adoption. She has authored blogs and workshops in AWS Data and Analytics.

Vinod Kisanagaram is an AWS Solutions Architect working with Enterprise Higher Education customers to craft highly scalable and resilient cloud architectures. He works closely with Research and Central IT teams in understanding their functional requirements and translating to technical implementation and brings on additional AWS technical expertise where needed. He’s authored blogs and workshops on AWS Observability.