Introduction to Machine Learning on Microsoft Azure

So, what is Machine learning?

Machine learning is embedded in our everyday activities. Machine learning is all about teaching a machine how to react based on data which it encounters. And as it continues to encounter this data, it must absorb that information and learn from it, like a child. Machine learning it rooted in a plethora of apps and software; from predicting the weather report to showing the best route out of the traffic to voice assistance.

This blog shares the basics of Machine Learning on Azure and why it is a platform which is recommended for performing data predictions.

Different Algorithms on Azure

Azure makes available a variety of different machine learning algorithms that can be utilized as per the application requirements. Typical machine learning algorithms fall under 3 types; supervised, unsupervised and reinforced. Azure has provision for all these types.

Supervised – this type of machine learning is used to predict the information based on already available data. The data is used to pre-train the model and the parameters between which the output should be, is known. It makes use of labelled data to predict the outcome.

Unsupervised – in this technique, no prior information has been provided to the model. This technique is useful when you don’t know what the outcome should look like cause the data is provided in an unlabeled format.

Reinforcement learning - this learning uses algorithms that learns from feedback that helps it determine if the choice the algorithm has made was correct, neutral, or incorrect. It’s a good technique to use for automated systems without human guidance.

In Azure machine learning, these 3 different algorithms are further divided into these following categories. These are different models that can be applied according to the use case:

• Linear regression
• Logistic regression
• Naïve Bayes
• Support Vector Machines (SVM)
• Decision tree
• K-Nearest neighbor
• Random forest
• K-Means

How it works on azure

The conventional flow of any machine learning algorithm is firstly pre-processing raw data in order to filter it by eliminating missing values, removing or reducing noisy data and checking other inconsistencies before executing it to the algorithm. The prepared data is then fed into the algorithm and models are generated. The ideal model is chosen which will be used for all other data sets.

But on Azure, there are several in-built resources that can be consumed to abate the efforts of these tasks.

Azure ML Studios is consumed for accelerating and managing the machine learning project lifecycle. It assists the consumer for pre-processing the data and carrying out the whole process efficiently. It benefits the users by minimizing the repetition of tasks and automates these tasks, carrying out bulk of the tasks which are repetitively.

Azure ML API is used to provide operations for managing datastores, environments, data assets and managing the code assets.

Benefits and drawbacks of Machine learning on Azure

Advantages:
• It increases productivity by accelerating the machine learning project lifecycle.
• Reproducibility also increases as Azure will handle the process to pre-process data, deploy and monitor the models.
• Reliability is improved as Azure cloud platform is secure.
• Connectivity makes it available for several users to work on the same application at the same time.
• Monitorability makes it easier to visualize the response and progress of the models.
• Low or no coding is involved, making it easier for non-technical people to get started with machine learning.
• Azure makes it easier to convert to production-ready with simply a click.

Disadvantages:
• Azure machine learning has several pre-trained models but not all so fewer algorithms and transformations are built-in.
• As Azure is a cloud platform, it will charge money for utilizing its resources.
• Running the models will consume more computing power and storage as the model builds results.
• Azure ML is a high-level tool, which therefore grants you less control over the model which is built.

Conclusion:

Azure provides several machine learning resources which can be utilized to benefit the user based on the application. There are a few cons but the pros outweigh them because these pros provide functionalities which will help the user to progress.

It is very good for working in a collaborative teams in which the team members with zero coding experience can view the same data. Azure provides industry leading machining operation which allows the models to be monitored after they have been deployed and trained. It can integrate with other open-source machine learning platforms such as tensor flow or scikit-learn. Moreover, the environment that is created to host the pipelines and operations are scalable, robust and secure.

Therefore, Azure machine learning is a great tool to develop machine learning application and utilize those resources to find data patterns, prediction and flaws.