Big Data

Anomaly Detection with Azure Machine Learning Studio.

We knew Azure is one of the fastest growing Cloud services around the world it helps developers and IT Professionals to create and manage their applications. When Azure HDInsight has huge success in Hadoop based technology, For Marketing Leaders in Big data Microsoft has taken another step and introduced Azure Machine Learning which is also called as “Azure ML”. After the release of Azure ML, the developers feel easy to build applications and Azure ML run’s under a public cloud by this user need not to download any external hardware or software.

Azure Machine Learning is combined in the development environment which is renamed as Azure ML Studio. The main reason to introduce Azure ML to make users to create a data models without the help of data science background, In Azure ML Data models, are Created with end-to-end services were as  ML Studio is used to build and test by using drag-and-drop and also we can deploy analytics solution for our data’s too.

Anomaly Detection the name itself say’s finding the data anomalously this application is used by different industries around the world. This application is used for finding issues in software, Perform quality control & some of the hacking details…etc. In the further discussion, we will know more about Anomaly Detection with Azure Machine Learning.

First, What is Azure Machine Learning?

It is the process of integrating with data science and cloud analytics solutions.  This can be used for deploying experiments for different models at cloud scale. Machine Learning uses algorithms that operate by building a model from data inputs and a set of features to make predictions on the output. The main elements of Azure machine learning  are:

  1. Workbench
  2. Experimentation Service
  3. Model Management Service(MMS)
  4. MMLSpark Library
  5. Tool for AI(Artificial Intelligence)

With the help of above elements and services, Data Science project can speed up development and deployment.

Example – Let’s take office in that IT Employee and Machine are two different features, Were Employee makes a mistake and he learns from it were as machine need some regular instructions to perform any task which can’t be done by its own. Now the question is – can a machine learn from its mistakes and answer for the question is yes and the process name is known as Machine Learning.

What is Azure Machine Learning Studio?

It is a web-based integrated development environment(IDE) for experimenting the newly developing data, Using this we can easily deploy and develop the modules and services compare to other Azure cloud services.

Where is Azure Machine Learning Studio used:

  1. Data Science
  2. Analytic Solutions
  3. Cloud Services.


  • We can perform Network Analysis
  • Healthcare Departments
  • Optimizing Web applications
  • Detection of Fraud.

Azure Anomaly Detection with Machine Learning Studio

Now let’s talk about Anomaly Detection, This has been introduced long back without Machine Learning. It is an API created with Azure Machine Learning(ML) which is used for finding the different types of anomalous patterns in Data series it is also known as outliers.

This Service’s are used by top IT industry’s were they have massive data to be followed for different information such as health records, the performance of the system, maintaining the dashboards and many other things to do.  Were as collecting the data is not entirely automated it has some error’s too, So it’s tough to make error-free at given period.

When Anomaly Detection uses  Machine learning to solve the error’s maintain the larger data safe, it provides end-to-end pipeline so that it’s very difficult to lose the important data of that particular industry and this also monitor’s every data from starting to ending which is also called as a downstream process. There are two techniques in machine learning for effective anomaly detection.

By using Machine learning, we can find error’s very quickly so that problems can be solved in a given time. The issues can be:

  1. IT infrastructure
  2. Services(SLA variations)
  3. Login and Payment Failures

Benefits: Azure Machine Learning

  • By making use of this tool, it’s very easy to import training data and results are accurate.
  • The Data Model can be published in web services
  • Azure Machine Learning is very user-friendly and comes with a set of tools.
  • Pay for what you use; Azure Machine Learning comes with a flexible cost.
  • In Azure ML, has no data limit to import data from Azure storages and hdfs systems.  


Azure Machine Learning Studio is being widely used by large-scale industries were they have large Data to be stored. Azure Machine learning has a wide range of applications in almost every domain. In this you learn about the benefits and applications of Azure Machine Learning, Azure Anomaly Detection with Machine Learning Studio was explained. There are a lot of opportunities in Azure Machine Learning, People who whats to build the career take training on Azure Machine Learning.  

About the Author:

GnanaSekar is working as a Technical Content Contributor & SEO Analyst for Mindmajix. He holds a Bachelor’s degree in Electrical & Electronics Engineering from Anna University. He can be contacted at Connect with Gnanasekar on Twitter and Linkedin.

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