In this project we attempted to predict the presence of viral pneumonia in computer tomography (CT) scans. Our data was a subset of the MosMedData: Chest CT Scans with COVID-19 Related Findings Data. The subset consists of 200 CT scans with half of it normal and half of it with 50-75% lungs scarring. In the notebook you will see the following:
Loading the Data Processing the Data (normalizing, resizing, etc) Train-Test Split Building a 3D CNN Model using Keras Testing the Model Creating the Model Artifact Saving the model in OCI Model Catalog
It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. We got this dataset from kaggle.com The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. It contains only numerical input variables which are the result of a PCA transformation.
In this use case we will accomplish the following tasks:
Loading data to Object Storage Pulling data from object storage using ADS Preprocessing data Using AutoML to generate and train ML models Testing ML model (notebook) Saving ML model to model catalog Deploying ML model as a CLI, REST API on OCI