machine learning

3 Ways To Use Machine Learning: December 2021 Hackathon Overview

With a new emphasis on machine learning, our December 2021 Hackathon focused on building your own machine learning model. Learn about what our Toads did.

At the end of 2021, Metal Toad leaders came together to reevaluate the future of Metal Toad. After several internal meetings, it was decided that Metal Toad's future is with machine learning. The main driver in this decision is that machine learning is on the rise. Businesses across every vertical are either already using it, want to use it, or have seen the capabilities it holds. From languaging processing, forecasting, customer support, and more, machine learning will continue to rise in popularity and be integrated into all aspects of business operations. 

With a new focus comes a new vision. The leadership team at Metal Toad decided that Metal Toad's new vision would be to be the world's leading Cloud Partner for AWS machine learning & media solutions. This statement combines the future of Metal Toad with our current experience working with businesses on media solutions.

With a new vision in hand, our December 2021 Hackathon was the perfect opportunity for our Toads to dive headfirst into machine learning. The theme for this year's hackathon was to build your own machine learning model. We had three teams participating; Team Brazuca, Team Gambiarra, and Team Coffee Lovers. All three teams tackled very different challenges that machine learning can solve. 

Identifying Downed Power Lines

For their December Hackathon 2021, Team Brazuca demonstrates the process of identifying downed power lines With AWS Rekognition Custom Labels. With AWS Rekognition Custom Labels, you can identify the objects and scenes in images. The Toads in this team were Nathan Wilkerson, Marcelo Beiral, Jessica Souza, Allen Louie, and Scott McAuliffe. 

Implementing AWS DeepRacer

Team Gambiarra demonstrates the process of implementing AWS DeepRacer, an autonomous 1/18th scale race car designed to test reinforcement learning (RL) models by racing on a physical track. The Toads in this team were Lauro de Lacerda, David Dolan, Vinícus Mussato, and Michael Ellis.

Identifying Coffee

Team Coffee Lovers demonstrates the process of identifying espresso and long black coffee using Amazon SageMaker. With Amazon SageMaker, you can perform all machine learning development steps on this cloud machine-learning platform. The Toads in this team were Nadia Fornaro, Laura Boemo, Simon Scudder, Patricia Dias, Laura Chaves, and Helder Nascimento.

Curiuos to learn more about machine learning or about our hackathons? Here are some articles to help you get started:

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