Build, Train and Deploy ML Models at Scale with Amazon SageMaker
When: Oct 8th, 9:30 a.m. -12:30 p.m.
Where: UCSD Qualcomm Institute, Atkinson Hall Auditorium
Presented by AWS
During this event:
- 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.
Register for Build, Train and Deploy ML Models at Scale with Amazon SageMaker Below
Registration for this event has ended.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:
- AWS
- Python
- Jupyter notebooks
- Basic machine learning methods
Prereqs for Workshop*
- AWS Account (already created)
- Access to SageMaker, S3, ECR from your IAM role.
- 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 AWS account, please open an account following the directions on Blink here: https://blink.ucsd.edu/technology/cloud/aws/ (a $0 PO is fine.). If you have questions, please email matsonh@amazon.com.