This project 'Sagemaker Deployment' which consists in deploying a Sentiment Analysis model using RNN in the Amazon AWS SageMaker tool. The notebook and Python files provided here result in a simple web app which interacts with a deployed recurrent neural network performing sentiment analysis on movie reviews.
In the final architecture AWS API Gateway and AWS Lambda functions is used as well. The application architecture diagram is:
The notebooks provided in this repository are intended to be executed using Amazon's SageMaker platform. The following is a brief set of instructions on setting up a managed notebook instance using SageMaker, from which the notebooks can be completed and run.
Click on 'Create notebook instance'. Change the region to North Virginia and apply for limit increase of ml.p2.xlarge(sagemaker) instance to 1. Also we need to apply limit increase of ml.p2.xlarge training (sagemaker training) to 1. Additionally We need to create IAM role, Lambda function, API gateway. Instructions are provided in the notebook.
- Use git to clone the repository into the notebook instance
- git clone https://github.com/himanshumangal09/Sentiment-Analyis-on-AWS.git
After you have finished, close the terminal window.
Open and run the notebook jupyter notebook SageMaker Project.ipynb
The final project will be executed in a simple html page which can be deployed anywhere.
You will see the following:
