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A recurrent neural network deployed on SageMaker for determining the sentiment of a movie review

License: GNU General Public License v3.0

HTML 1.03% Jupyter Notebook 56.72% Python 37.10% Dockerfile 0.12% CSS 0.55% JavaScript 4.48%
recurrent-neural-networks sagemaker-deployment lstm-neural-networks deployments sentiment-analysis

deploying-a-sentiment-analysis-model's Introduction

Sentiment Analysis

Orchestrated a recurrent neural network for the purpose of determining the sentiment of a movie review using the IMDB data set. The model is trained using Amazon's SageMaker. In addition, I deployed the model and construct a simple web app to interact with the deployed model.

Access the website

Try it out

Access the website deployed using AWS S3 bucket. Type your review and submit to examine its sentiment.

How it works

model/ : Contains the output of training the model on SageMaker. train/ : Contains model definition and code to train pipeline to train the model. serve/ : Contains code for deploying an endpoint to AWS Lambda or SageMaker used for inferencing. website/ : A ReactJs app to try it out. The app consumes the trained model instance deployed on AWS Lambda.

See how to train the model

If you are interested in training a yourself, clone this repository to a SageMaker notebook instance.

Run all code cells in the notebook (This will take a very long time to run on a CPU, preferably you should run on a GPU instance eg ml.m4.xlarger).

If your interested in creating an end-point to the deployed model, go through the instruction in step 7 of the notebook.

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