Machine Learning - Creating, Training/Testing and Deploying Models on Amazon SageMaker

Francis Odo

After learning how to create, train/test and deploy a Machine Learning model locally in a non-production environment, it is also necessary to learn and understand how to process it on a particular cloud system. My focus here is on the Amazon Sagemaker and Google ML Cloud. These are all Artificial Intellegience architecture based applications.

First, The Amazon SageMaker Cloud System There are two different projects packaged here.

1. Sentiment Analysis

The project files are:

a) SageMaker Project.ipynb

b) train.py

c) predict.py

2. Plagiarism Detection

a) Plagiarism_Feature_Engineering.ipynb

b) Training_a_Model.ipynb

c) helpers.py

d) train.py

Second, The Google ML Cloud System