Finally, I started MS (Microsoft) Azure ML (Machine Learning). So in this blog, I would like to report what it is and why it is amazing for not only data scientists but also businessmen/women. MS Azure ML is a kind of ML services on the cloud. It is easy to start data analysis by ML, even for beginners. For data analysis, it is critically important to have seamless processes 1. Data 2. Models 3. Output. Unfortunately, most of ML services are provided as an independent one from other services, therefore users should gather data and inform results of data analysis of stakeholders and management, one by one, independently, outside ML services. However, when we see the portal of MS Azure, Machine Learning is built on as one of the functions in MS Azure. So we can operate this ML as one of the processes in MS Azure. It is completely different from other ML services. Then let me go to MS Azure ML studio and look at major functions in details.
After creating ML working space, we can go to ML studio where experiments can be done by using Graphical User Interface.
More than 30 data sets, for example census income data, are set up in advance. So beginners can start data analysis immediately for training. It is good because they can concentrate on data analysis in MS Azure ML. Data, which are analyzed, should be just dragged and dropped into experiment area. So data can be handled with their intuition. No need to read manuals in advance.
2. Predictive models
In ML studio, there are more than 10 predictive models for classification. Logistic regression, neural network and SVM., etc. are available here. Models for regression and clustering are also available. According to the documents, more than 300 R packages, which are open source in R language, are also available. It is amazing that these models can be used by drug and drop in ML studio without writing code. So beginners can analyze data without coding the models.
Once data analysis is completed and predictive models are developed, it is easy to release it as web application services by clicking the buttons to deploy it in the web. It is usually difficult to explain how predictive models work just by theory. Web applications must be powerful tools to explain how the models work tostakeholders, managements and customers because web applications can show us the results based on inputs from users.
As I said before, it is critically important to have seamless process 1. Data 2. Model 3. Output. Microsoft Azure ML realizes this as a cloud service. I would like to develop interesting web services based on Machine Learning in the future. The current version of MS Azure ML is a preview, so functionalities might be changed or removed, added going forward. If you need more information about MS Azure ML, please refer to this web. Let us enjoy machine learning !