dhavalpotdar / Sentiment-Analysis-Using-Amazon-SageMaker

This repo contains finished code for the deployment project of Udacity's Machine Learning Engineer Nanodegree

Sentiment-Analysis-Using-Amazon-SageMaker

This repo contains finished code for the deployment project of Udacity's Machine Learning Engineer Nanodegree.

This is the workflow followed:

1. Download or otherwise retrieve the data.
2. Process / Prepare the data.
3. Upload the processed data to S3.
4. Train a chosen model.
5. Test the trained model (typically using a batch transform job).
6. Deploy the trained model.
7. Use the deployed model.

Data

The IMDB Reviews dataset can be downloaded as a .tar file from this link.

Finished App

Following is the schematic layout of the app:

On the far right is the model which is deployed using SageMaker. The model is a custom Pytorch LSTM model. On the far left is the web app that collects a user's movie review, sends it off and expects a positive or negative sentiment in return. In the middle lies a Lambda function, that can be executed whenever a specified event occurs. This function is given permission to send and recieve data from a SageMaker endpoint.

End Product:

An accuracy of 82.9% is achieved.