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.

How an email system tracks the spam email.
How an online shopping system shows the similar product which you are looking for.

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.

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.

Image Source – Microsoft Docs

Image Source – Microsoft Docs

Reference Links

https://en.wikipedia.org/wiki/Machine_learning
https://www.internetsociety.org/resources/doc/2017/artificial-intelligence-and-machine-learning-policy-paper/?gclid=EAIaIQobChMIn56dqfXA4wIVENbACh0njwYVEAAYAyAAEgJYRfD_BwE
https://www.expertsystem.com/machine-learning-definition/
https://www.edureka.co/blog/what-is-machine-learning/
https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml

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