How to deploy Machine Learning model as a Rest API? | Flask Rest API | Integrate in existing web app

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In this video you will learn how to deploy your Machine Learning models as a REST API. The deployment of your models is a crucial step in the ML workflow and it is the point when your models actually become useful to your company.
I will cover how to convert your Model to a REST API so the users can consume them in a production environment. We will focus on solutions converting the model to a FLASK REST API. I will cover the Simple Linear Regression model but the concepts can be easily transferred to other Models and frameworks.

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#MachineLearning #RESTAPI #SimpleLinearRegression

Topics covered in this video:
0:00 - Agenda : Introduction
1:05 - Preview of complete app
1:37 - Get Model file
1:54 - Create new project + setup
3:41 - Model prediction class
5:42 - Get Model data class
6:22 - Run & Test API
6:55 Rest API Web integration

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Комментарии

The power of ai model along with event based architectures is so powerful. Thank you for this tutorial.

brunowario

It would have been very nice to show ALL the STEPs including the file in the directory you mentioned. That will make it easy to understand and follow along.

Abioladavid

What to do incase of 2 parameters in get function? My model takes 2 features as input.

vaibhavchaudhary

Do we use .pkl file in production too ? Is this the same flow.

AnsumanSingh

create a video please on how to host an API this on azure

mojtabasarvari

how to make the model into pickle file?

adithyaas

you could have showed how that pickle file was made

adithyaas

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