Team Hackathon Challenge #33: Machine Learning with Amazon SageMaker


Details
Welcome to λ#, the Serverless AWS Lambda community. This event is part workshop, part challenge, part competition, and always a rush!
This is a hands-on event suitable for passionate beginners, advanced developers, and curious minds alike. Each hackathon is focused on exploring a serverless technology by building a solution with it. Participants are split into random teams of 2 to 4 members to collaborate on the challenge.
In this challenge, we'll train and deploy a machine learning model using AWS SageMaker. The model will use a powerful text classification algorithm to be able to predict the sentiment of product reviews on Amazon. You'll learn the importance of properly grooming the training-data, testing your model, and making fun predictions that yield surprisingly and accurate results!
BRING YOUR LAPTOP!
Be prepared and make sure you have the following applications installed on your computer:
- Sign-up for an AWS account: https://aws.amazon.com/
- Install AWS CLI: https://aws.amazon.com/cli/
- Install .NET Core 2.1: https://www.microsoft.com/net/download
- Install the λ# CLI: https://github.com/LambdaSharp/LambdaSharpTool
Mentors are available to answer questions and guide each team to a solution. This is a great event to meet experienced engineers, build cutting edge services, and mingle with like-minded technologists who are passionate about serverless architecture!
WHAT TO EXPECT!
• Fearless collaboration
• Problem solving under duress
• Celebrating accomplishments
SCHEDULE
• 6:00 - 6:30 Networking & Happy Hour
• 6:30 - 7:00 Presentation & Setup
• 7:00 - 8:00 Team Hackathon Part 1
• 8:00 - 8:15 Stretch & Share
• 8:15 - 9:15 Team Hackathon Part 2
• 9:15 - 9:45 Present & Celebrate Insights
HOW TO GET THERE!
The MindTouch office is located on Broadway, between Front and First Street. There are nearby trolley and bus stations.
FOLLOW λ#
• TWITTER: https://twitter.com/LambdaSharp
• GITHUB: https://github.com/LambdaSharp

Team Hackathon Challenge #33: Machine Learning with Amazon SageMaker