Deploy your first end-to-end ML model using Streamlit

Deploy your first end-to-end ML model using Streamlit

Deploy your first end-to-end ML model using Streamlit. I am going to deploy a Supervised machine learning model to predict the age of a Abalone and in the next part of the tutorial we will host this web app on Heroku.

Imagine building a supervised machine learning ML model to decide whether a credit card transaction has detected fraud or not. With the model confidence level in successful applications, we can evaluate the risk-free credit cards transactions. So you have built the model, which can detect credit card frauds, now what? The deployment of such ML-model is the prime goal of the project.

Deploying an ML-model simply means the integration of the model into an existing production environment which can take in an input and return an output that can be used in making practical business decisions. Here is where Streamlit comes to play !

Streamlit is a open-source app framework__is__the easiest way for data scientists and machine learning engineers to create beautiful, performant apps in only a few hours! All in pure Python. All for free.

In the part one of this tutorial I am going to deploy a Supervised machine learning model to predict the age of a Abalone and in the next part of the tutorial we will host this web app on Heroku. An Abalone is a molluscs with a peculiar ear-shaped shell lined of mother of pearl. Abalone’s age can be obtained using their physical measurement. Let us deploy the model.

machine-learning supervised-learning deploy deployment-model streamlit

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