Lead the innovation and development of applications capable of predicting trajectories, maintenance and status of machinery elements, given real-time and historical data obtained with a cloud of sensors including pressure, force, velocity and temperature. This was the starting point for the creation of machine learning algorithms to predict the quality of the final product.
The framework developed for the real-time and historical data processing led to the design and development of bespoke applications to improve the customer's interaction with products, such as automatically generated bi-annual detailed reports containing information that span from energetical consumption to machine wear and expected maintenance.
Manage and lead the transfer process for a local Linux to a server hosted in AWS
Work on the implementation of AWS data lake and AWS deep learning AMIs to create algoritms capable of predicting future maintenance dates and possible issues.
