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 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
• chemistry, DeepChem: building an online compound solubility prediction workflow
• genomics, 1000 genomes dataset

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 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.

Food: Pizza will be served.

Intended audience

Machine Learning Practitioners old and new: developers, scientists, data science practitioners, research staff, and any other interested persons. Participants should have some familiarity with:

• python
• jupyter notebooks
• basic machine learning methods

Agenda

10:00:  Speaker and Facilitator Introductions
10:05:  Introduction to Amazon Sagemaker
10:25:  Environment Setup
10:45:  Lab 1 – Digit Classification with the Amazon Linear Learner Algorithm; guided walk-through and recap
11:15:  Lab 2 – Distributed Training with TensorFlow (self-guided)
11:45:  Lab 3 – How to Bring Your Own Model (self-guided)
12:15:  Break
12:20:  Lab 4 – Using Public Datasets (self-guided)
12:50:  Closing and pizza

Prereqs for Workshop

1. AWS Account (already created)
2. Access to SageMaker, S3, ECR from your IAM role.
3. Access to SageMaker service role AmazonSageMaker-ExecutionRole or ability to create IAM roles.

Please make sure to have these taken care of prior to the workshop.  If you do not have an account, please open an account following the directions on Software Central here:  (a $0 PO is fine if you don’t want to use a credit card.)  https://softwarecentral.ucla.edu/amazon-aws-howto.  If you have questions, please email matsonh@amazon.com.

Note:  AWS will provide credits so you will not incur charges related to the workshop (as long as you shut down any resources running in your account after the workshop concludes).

RSVP by visiting the following link: RSVP form