Mr Ben Thurgood1
1Amazon Web Services, Barton, Australia
In this workshop participants learn to solve Machine / Deep Learning problems using the tools available in the Amazon Web Services (AWS) cloud. The development and application of machine learning models is a vital part of scientific and technical computing. Increasing model training data size generally improves model prediction and performance but deploying models at scale is a challenge.
Participants will learn to use Amazon SageMaker, a new AWS service that simplifies the machine learning process and enables training on cloud stored datasets at any scale.
Applications will include:
- satellite imagery, MXNet, LandSat dataset: automatically mapping buildings in Vietnam
- genomics, 1000 genomes dataset: TBD
The workshop will walk attendees through the process of building a model, training it, and applying it for prediction. Working in web-based Jupyter Notebooks powered by AWS, we’ll explore common algorithms (e.g. k-means and PCA) and deep learning with MXNet and TensorFlow. Participants will become familiar with SDKs for Python and Spark and other APIs that make machine learning with AWS easy to use.
With Amazon SageMaker, users take their code and analysis to the data, and participants will experiment on real-world datasets, such as Earth on AWS and the Cancer Genome Atlas. At the end of the session, attendees will have the resources and experience to start using Amazon SageMaker and other AWS services to accelerate their scientific research and time to discovery.
Biography:
Ben Thurgood is a Principal Solutions Architect with the Public Sector team in Sydney. He works with customers in genomics, research, utilities, health care, education and government – helping them to take advantage of the AWS platform. Ben has a software engineering background and has led large scale transformation projects. Over the last 12 months Ben has been specialising in Machine Learning, Deep Learning and AI services at AWS.