Added model deployment and automated releases

Jithsaavvy · Feb 1, 2023 · c039d01 · c039d01
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diff --git a/README.md b/README.md
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 # Sentiment analysis from MLOps paradigm
 
+![benchmark](https://github.com/Jithsaavvy/Sentiment-analysis-from-MLOps-paradigm/workflows/Test%20and%20benchmark%20models/badge.svg)
+![deploy](https://github.com/Jithsaavvy/Sentiment-analysis-from-MLOps-paradigm/workflows/Deploy%20to%20sagemaker/badge.svg)
+![releases](https://img.shields.io/github/v/release/Jithsaavvy/Sentiment-analysis-from-MLOps-paradigm)
+
 This project promulgates an **automated end-to-end ML pipeline** that trains a **bi-directional LSTM** network for sentiment analysis task, **tracks** experiments, **pushes** trained models to **model registry**, benchmark them by means of **model testing** and **evaluation**, pushes the best model into production followed by **dockerizing** the production model artifacts into a deployable image and **deploys** the same into cloud instance via **CI/CD**. 
 
 ## Author
@@ -271,6 +275,10 @@ sagemaker._deploy(
                 )
 ```
 
+| ![flowchart](./images/Sagemaker_endpoint.jpg) |
+|:--:|
+| <b>Figure 9: Production model deployed to AWS Sagemaker </b>|
+
 **Note:** <br>
 *Every AWS resources created for this project will be deleted after the pipeline is executed successfully. This is done on purpose, to restrict and limit any incurring additional cost!!*
 

diff --git a/images/Sagemaker_endpoint.jpg b/images/Sagemaker_endpoint.jpg