Team Number: 65
During the COVID-19 pandemic hospitals have been over-working their staff and as a result quality of care has taken a hit. One of the common ailments associated with contracting COVID-19 is pneumonia, which is a deadly combination.
What it does
The 50 Shades of Grey Project uses a Machine Learning Model to determine whether or not an X-ray shows symptoms of pneumonia. Much of the backend processing is done through DCP, which allows for quick distributed cloud processing.
How I built it
The dataset was hosted on the Google Cloud Platform in the GCS bucket. I made the dataset public to allow for easy access in my notebook. I found a couple articles, research papers, and official Tensorflow documentation to create the Machine Learning Model. I used the DCP API to distribute the work required to process the data for the machine learning model to digest it.
Challenges I ran into
The DCP API does not support a lot of features that data scientists would find useful. Furthermore the platform is in a extremely beta form and the lack of documentation made the discovery process more difficult.
Accomplishments that I'm proud of
I'm proud that my model achieved a 98% recall rate, and a 70% accuracy on the testing dataset.
What I learned
I learned how to data engineer using parallelization, as well as I figured out how to build, train, and export a machine learning model in Tensorflow.
What's next for 50 Shades of Grey
Developing better support for parallelization for data scientists in conjunction with the DCP Platform.