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Scalable Machine Learning in the AWS Cloud
Wednesday, April 18th from 9am-1pm
Presented by Amazon Web Services & Yale Center for Research Computing
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During this live event, you will learn about:
- Introduction to AWS Sagemaker
- Typical machine learning life cycle - get data, train model, deploy model
- Walk through of envrionment set-up & build simple model
- Public data sets on AWS
- Going beyond, facilitating SDKs & APIs
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Register Here
Registration for this event has ended. |
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In this tutorial 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 fully managed AWS service that simplifies the machine learning process and enables training on cloud stored datasets at any scale.
The tutorial 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 a cloud-first paradigm for common algorithms (e.g. k-means and PCA) and deep learning with MXNet and TensorFlow. Participants will also become familiar with SDKs for Python and Spark as well as 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, offered in the AWS Open Data program. At the end of the session, attendees will receive an AWS credit voucher, and have enough to get started using Amazon SageMaker and other AWS services for their own scientific research. |
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