Learn Azure Machine Learning SDK with Dr G

Table of Contents

1. Introduction
2. Azure Machine Learning with SDK
3. Setting up Azure Machine Learning Workspace
4. Preprocessing the Data
   1. Data Cleaning
   2. Feature Engineering
5. Splitting the Data for Training and Testing
6. AutoML in Azure Machine Learning
   1. Configuring AutoML
   2. Running the AutoML Experiment
   3. Evaluating the Models
7. Deploying the Model
8. Conclusion

Introduction

In this article, we will explore Azure Machine Learning and how it can be used to build and deploy machine learning models using the Azure Machine Learning SDK. We will walk through the process of setting up an Azure Machine Learning workspace, preprocessing the data, splitting the data for training and testing, running an AutoML experiment, evaluating the models, and deploying the model. By the end of this article, You will have a good understanding of how to use Azure Machine Learning to build and deploy machine learning models.

Azure Machine Learning with SDK

Azure Machine Learning is a cloud-Based service that allows you to build, train, and deploy machine learning models. It provides a wide range of tools and services to help you automate the end-to-end machine learning lifecycle. The Azure Machine Learning SDK is a Python library that allows you to Interact with Azure Machine Learning services and resources programmatically.

Setting up Azure Machine Learning Workspace

Before we can start using Azure Machine Learning, we need to set up an Azure Machine Learning workspace. The workspace serves as a centralized location for managing all the resources and artifacts required for machine learning experiments. To set up a workspace, you need an Azure subscription. If you don't have an Azure subscription, you can sign up for a free trial.

To Create a workspace, you can use the Azure portal or the Azure Machine Learning SDK. The SDK provides a convenient way to create and manage workspaces programmatically. Once you have created a workspace, you can access it using the SDK and perform various tasks such as data preparation, model training, and deployment.

Preprocessing the Data

Before we can train a machine learning model, we need to preprocess the data. Data preprocessing involves cleaning the data, handling missing values, and transforming the data into a format suitable for training our model.

Data Cleaning

Data cleaning is the process of removing or fixing any errors, inconsistencies, or outliers in the data. This may involve removing duplicate records, handling missing values, or fixing incorrect values. By cleaning the data, we can ensure that our model is trained on high-quality and reliable data.

Feature Engineering

Feature engineering is the process of creating new features or transforming existing features to improve the performance of our model. This may involve creating new variables, deriving new features from existing variables, or encoding categorical variables. By engineering the features, we can provide our model with more Meaningful information and improve its predictive power.

Splitting the Data for Training and Testing

To evaluate the performance of our machine learning model, we need to split the data into training and testing sets. The training set is used to train the model, while the testing set is used to evaluate the model's performance on unseen data. By evaluating the model on unseen data, we can get an estimate of how well it will perform in the real world.

AutoML in Azure Machine Learning

AutoML (Automated Machine Learning) is a feature in Azure Machine Learning that automates the process of building and training machine learning models. It automatically explores different algorithms and hyperparameters to find the best model for our data. In Azure Machine Learning, AutoML can be performed using the AutoMLConfig class.

Configuring AutoML

To configure AutoML, we need to specify the Type of task (classification or regression) and the primary metric to optimize. We can also specify various other settings such as the maximum amount of time the experiment should run, the number of cross-validations to perform, and so on.

Running the AutoML Experiment

Once we have configured AutoML, we can run the experiment and let Azure Machine Learning automatically explore different models and hyperparameters. AutoML will train and evaluate multiple models and provide us with a leaderboard ranking the models based on performance.

Evaluating the Models

After running the AutoML experiment, we can evaluate the models and compare their performance. We can look at metrics such as accuracy, mean squared error, or any other suitable metric depending on the task. By evaluating the models, we can choose the best-performing model for deployment.

Deploying the Model

Once we have selected the best-performing model, we can deploy it to make predictions on new data. Azure Machine Learning provides various deployment options, such as deploying the model as a web service, batch scoring, or deploying to edge devices. The deployment process involves creating a scoring script, creating an environment, and deploying the model to a target environment.

Conclusion

In this article, we have explored how to use Azure Machine Learning to build and deploy machine learning models. We have covered topics such as setting up an Azure Machine Learning workspace, preprocessing the data, splitting the data for training and testing, running an AutoML experiment, evaluating the models, and deploying the model. By following the steps outlined in this article, you should be able to leverage Azure Machine Learning to build and deploy your own machine learning models.