In the previous post I have discussed how to create an Azure Machine Model. In this post I will be discussing how to Deploy this model.
Prerequisites
Before deploying a machine learning model in Azure, there are several prerequisites you need to fulfill:
Prepare your data: You should have a well-prepared and cleaned dataset that has been tested and validated.
Select your model: You need to choose an appropriate machine learning algorithm based on your problem statement and the nature of your data.
Train your model: You need to train your machine learning model on your prepared dataset.
As you must have seen we have undertaken all these steps and have trained our model in Azure Machine Learning in the previous post.
As part of training the model, we have created an inference pipeline. Now if we want to deploy the model, we need to create a real time inference pipeline.
In order to do that, in the Azure Portal launch the Azure Machine Learning studio as shown below.
