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Data Science on AWS

June 18, 2020 @ 12:00 pm - 1:30 pm EDT

You’re Invited!

During this session, we will show you how to build real-world machine learning pipelines using services such as Amazon SageMaker, Step Functions, Elastic Kubernetes Service (EKS), Kubeflow and much more!

Amazon SageMaker is a fully-managed service that enables data scientists and developers to easily build, train, and deploy machine learning (ML) models, at scale. Typically, a ML workflow consists of multiple steps. First, you gather data, then you explore and pre-process the data. You train and optimize the machine learning model. Finally, you deploy the model into production. We will take you through these steps demonstrating services such as Amazon Athena, Augmented AI and new features of Amazon SageMaker such as Autopilot, Processing Jobs, Experiments Tracking, Model Debugger and Model Monitor. You will learn how to use the Data Science SDK for AWS Step Functions to automate machine learning pipelines, and how to build Kubernetes-native machine learning pipelines with Kubeflow and Amazon EKS.

You can use this session to help embark on a self-paced, hands-on learning path or join us for a free, all-day, hands-on virtual workshop offered once a month.

Please register at https://datascienceonaws0620.splashthat.com/

Who should attend?

Developers, solutions architects, and technical end users who want to understand the fundamentals of the AWS machine learning stack.

Data scientists, machine learning engineers, data engineers and data analysts looking to accelerate their AI/ML initiatives.

Machine learning enthusiasts, women in technology or big data looking to jump-start their career in AI/ML technologies.

What do I need to know before attending?

Basic familiarity with the concepts of data science and machine learning, as well as a general understanding of cloud technologies are recommended pre-requisites for this session.

Basic familiarity with Python is helpful to follow the code examples.


June 18, 2020
12:00 pm - 1:30 pm EDT