Workshops & Seminars | AWS No-code machine learning using Sage Maker Canvas (In Person)
Juan Carlos Bedoya
Senior Solutions Architect, AWS
Juan Carlos Bedoya is a Senior Solutions Architect for AWS based in Brisbane Australia, Australia. He works with AWS’s Health customers helping them with their transformation journey to sustainable patient centric care. Juan is also interested in AI and its potential in Healthcare. Prior to AWS, he worked for over 20 years in networking, voice technologies and customer engagement.
Healthcare Technical Lead for Asia Pacific & Japan, AWS
Eleni Dimokidis is the Healthcare Technical Leader for Asia Pacific at Amazon Web Services. Her works focuses on helping healthcare customers transform their businesses through cloud computing technology and positively impact world health.
Senior Machine Learning Solutions Architect, AWS
Industry Solutions Architect, AWS
Noor is an AWS Industry Solutions Architect based in the APJ region. She is based in Singapore and looks after education customers. Noor’s main interest is in helping educational institutions innovate in providing lifelong learning.
MD1 - Tahir Foundation Building, 12 Science Drive 2, Singapore 117549,
Level 8 Computer Lab1
*Limited Seats available
Registration is FREE!
Artificial intelligence (AI) and machine learning (ML) offer tools to help healthcare organisations extract value from vast amounts of patient data. Amazon SageMaker Canvas makes those tools and insights more accessible. Join this interactive chalk talk as we use SageMaker Canvas to analyse and clean a clinical dataset and build a predictive model, all without writing a single line of code.
- Machine Learning on Amazon’s AWS Sagemaker Canvas without writing any code
- Live project with sample dataset
- Training and testing Machine Learning (ML) Models, and improving accuracy