Azure Machine Learning

Pre-requisite Knowledge

Before we start with the understanding of what is Azure Databricks, we should have: 


I would like to give some short information about ‘what is artificial intelligence and machine learning’ before jumping in to Azure machine learning.
Artificial Intelligence
  • In simple words ‘Artificial Intelligence (AI)’ is the artificial creation of the system like a human who can observe, react, learn, plan and process instructions and provide intelligence on it.
  • It is a rapidly emerging technology and internet enabled technology.
  • Sometimes AI is also called Machine Learning.


Machine Learning
  • Machine learning is not new. It is subset of Artificial Intelligence (AI).
  • An algorithm is sequence of activities/actions/steps used to solve a problem.
  • Implementing the algorithm and its models is called machine learning in computer world.
  • Today, developing a new algorithm to instruct the computer to run it is the cornerstone of the advanced technology.
  • Important
    • Machine learning is a data science technique that allows computers to use existing data to forecast future behaviors, outcomes, and trends.
    • Machine learning works on the mathematical model and is built by using the sample data.
    • Machine Learning has the capability to learn and IMPROVE from experience WITHOUT being explicit programmed.
  • Examples
    • How an email system tracks the spam email.
    • How an online shopping system shows the similar product which you are looking for.
  • Types
    • Supervised Learning – We have trained the model by data sets.
    • Unsupervised Learning – Machine learning model learns the data and finds the patterns and relationships in the data. Based on the pattern and relationships the model is trained.
    • Reinforcement Learning – Machine learning model will find out the best outcome. It works on a  trial and error method. Once the model is trained then it's ready for predicting the new data.

How it works?

  • At a high level, a machine algorithm creates one model data based on the existing test data as input.
  • Pushes the new input data then the machine learning algorithm makes a prediction based on the model which was prepared in step 1 above.
  • This prediction is evaluated and if accepted then an algorithm is deployed.
  • If the prediction is not accepted, then machine learning is trained again with bigger training data.
  • Azure Machine Learning Service,

    • Microsoft Azure provides the cloud-based platform to the machine learning implementation and deployment.
    • Using Microsoft Azure ML feature, we can prepare data, train the model, test the model, deploy the model, manage and track the model.
    • We can scale out ML to the cloud using Azure ML.
    • Azure ML supports the open source technologies like PyTorch, TensorFlow, and scikit-learn.
    • This technology can be used in any ML type mentioned above.
    • Use the Azure Machine Learning Python SDK with open-source Python packages or use the visual interface.
    • It has a visual interface for experimenting and deployment with drag-n-drop.
    • Microsoft Azure has Azure Machine Learning Studio to implement, test, train and deploy the ML. Machine Learning Studio is a collaborative place for data science, predictive analytics, cloud resources, and your data meet.
 Azure Machine Learning
Image Source – Microsoft Docs
  • We can implement the ML algorithm and model using tools,
    • Visual Interface (Drag-n-drop modules)
    • Jupyter notebooks (We can use SDK)
    • Visual Studio Code Extension
Azure Machine Learning 
Image Source – Microsoft Docs
Reference Links


In this article we have learned the overview of Artificial Intelligence, Machine Learning and Azure Machine Learning Service.