Microsoft Azure – Getting started with Azure Machine Learning Service

Pre-requisite: Azure 

Azure is a cloud computing service created by Microsoft for building, testing, deploying, and managing applications and services. Azure Machine Learning is a fully managed cloud service to do the following tasks:

•  Cloud-based predictive analytics service.
•  Provides tools to create complete machine-learning solutions in the cloud.
•  Quick model creation and deployment using Azure ML Studio.
•  Allows Models to be deployed as web services.
•  Provides a large library of Pre-Built Machine learning algorithms and Modules.
•  Allows for extending your models with custom-built R and Python code.

If you want to log in to azure ml studio studio.azureml.net

Steps to Get Started With Azure Machine Learning Service:

1. Create an Azure account: If you don’t have an Azure account, you can sign up for a free account and get $200 in credits.
2. Create an Azure Machine Learning workspace: Once you have an Azure account, you can create an Azure Machine Learning workspace in the Azure portal. A workspace is a container for your machine learning resources, such as datasets, models, and experiments.
3. Create a compute target: A compute target is a compute resource that is used to run machine learning jobs, such as training and deployment. You can create a compute target in your workspace using one of the supported compute options, such as Azure Machine Learning Compute or Azure Kubernetes Service.
4. Create a dataset: A dataset is a collection of data that is used to train and test machine learning models. You can create a dataset in your workspace using one of the supported data storage options, such as Azure Blob Storage or Azure Data Lake Storage.
5. Create an experiment: An experiment is a set of machine learning tasks that are run on a compute target using a specific dataset. You can create an experiment in your workspace using one of the supported experiment types, such as a Python script or a Jupyter Notebook.
6. Train and deploy a model: Once you have created an experiment and run it on a compute target using a dataset, you can train a machine learning model and deploy it to a production environment. You can deploy a model using one of the supported deployment options, such as Azure Container Instances or Azure Kubernetes Service.

Workflow of Azure ML Project

Get The Data: It means making the raw data available for the experiment, this is logically the first step for any machine learning experiment and Azure ML is no different. Azure ML provides multiple options for making the data available for the experiment, you can use the enter data manually module to create a small single Column dataset adding the values rather than loading the data from the source in azure ml studio or by importing the data from existing cloud data services that could also outside the azure ml studio or use unpack zipped datasets to upload the data in the compressed format. 

Prepare The Data: azure ml provides various modules to prepare and transform the data. You can apply various filters by adding rows and columns in the data, clearing the missing data values, or even editing the metadata. For any dataset available for experimentation, we want to split it into training sets and test sets, this can be done by providing a split module.

Feature Selection: Azure ML provides a variety of ways such as filter-based selection, feature LDA, as well as permutation feature importance. Now within the filter-based feature selection, it does provide us with a variety of options such as Pearson correlation, chi-squared, and so on.

Choose And Apply Learning Algorithms: The next step is to choose and apply the algorithm. Azure ML provides a vast array of algorithms that we can apply.  These are already built in Azure ML, and we simply have to choose the algorithms.

Train And Evaluate The Models: Here are modules for training, scoring, and evaluating the experiment and the model that you have built. All we need to do is provide the appropriate input and output connections.

Use cases of Algorithm

Predicting Categories: “Do not make it as it is, these algorithms are based on certain scenarios.” All these scenarios are based on Microsoft Azure Machine Learning: Algorithm Cheat Sheet. Here, I’m explaining the cheat sheet. If the outcome is Binary which is Yes or No, Pass or Fail, or Success or failure then we will use a Two-Class Classification algorithm. If the result has multiple possible outcomes, then we will use the Multiple Class Classification algorithm.

Predicting Continuous Value: If we’ve to predict a continuous Value such as Sales or revenue forecast, stock price, Loyalty score, etc., then we will use Regression algorithms.

Finding Unusual Data Points: When we have to find some unusual Data Points such as Fraudulent transactions, abnormal readings from machines, etc. then we will use Anomaly Detection algorithms.

Discovering Structure: When we have to discover structure in such case we will use K-Means Clustering algorithms.

What to Consider While Choosing an Algorithm?

Choosing the right machine learning algorithm is crucial for achieving optimal performance on a given problem. Here are some factors to consider while choosing a machine-learning algorithm:

• Type of problem: At first, we need to analyze what type of program is this like whether it’s an Outcome in binary or an outcome that has multiple possibilities. or We’ve to Predict Continuous Value (Such as Sales or revenue forecast, stock price, Loyalty score, etc.) Or Finding Unusual Data Points Such as Fraudulent Transactions, abnormal readings from machines, etc.
• Size of the dataset: Another factor is the size of the dataset. Some algorithms, such as Decision Trees, can handle small datasets while others, such as Deep Learning, require large amounts of data to train effectively.
• The complexity of the dataset:  If the dataset has many features or interactions between features, you may need a more complex algorithm, such as Random Forests or Neural Networks.
• Interpretability: Some algorithms, such as Linear Regression or Decision Trees, are more interpretable than others, such as Neural Networks or Support Vector Machines. 
• Performance metrics: The performance metrics you want to optimize, such as accuracy, precision, recall, or F1 score, also influence the choice of algorithm. Some algorithms may perform better on certain metrics than others.
• Time and resource constraints: In the end, you need to consider the time and resource constraints of your problem. Some algorithms, like Gradient Boosting or Neural Networks, require more time and computational resources to train than others, such as Decision Trees or Naive Bayes.

The choice of a machine learning algorithm depends on a variety of factors that are specific to your problem and dataset. It’s important to experiment with different algorithms and evaluate their performance using appropriate metrics before choosing the best algorithm for your problem.