Machine Learning is a hot topic this year. Machine learning is defined as follows, Field of study that gives computers the ability to learn without being explicitly programmed, Arthur Samuel (1959). As a lot of data and computer resources are available with less costs recently, Machine learning is getting popular in the field of data analysis.
In academics, there is no doubt that Machine learning has a good performance in statistical computing. Then how about the real world? When we try to apply Machine learning to data analysis on a daily basis, there are two difficulties to cope with. One is making data sets for training and the other is implementations of the models on computers in order to obtain the results from the models. As the name of ‘learning’ suggests, the training data set is required so that computers can learn the data before models generate the results from observed data. In order to implement these processes, knowledge and expertise about data analytics are required to complete the tasks. It must take one week or one month depend on the availability of resources of data scientists.
So I thought it might be difficult to solve today’s problem within today before I have heard the announcement from Microsoft on 16 June 2014. This is about Microsoft Azure ML, which is Machine learning statistical tool operated on its platform “Azure“. Azure is one of the platforms on the cloud. So it competes with Google apps for business and AWS. Although the details is not disclosed yet, it looks like that Azure ML is better than other analytics tools in order to establish seamless processes frompreparing the data set to model implementation on computers, because Azure itself is a seamless process platform and Azure ML is a part of them, not exists independently. So users do not need to pay attentions to the relationship among each independent component in the platform. All they have to do is just to consume data and obtain the results. It means that we can go short cut in analyzing data on a daily basis. It is critically important because the quick response against change of business environment is required in data driven management. Microsoft says that the preview of Azure ML will be started in July. It must be exciting and must enhance the data driven management.
Once Machine learning is getting a user-friendly tool, what should business managers do? I think it is very important to realize what data around us is available and will be in future. Data is a starting point for data analysis and data which is available has increased exponentially. ‘The data we create and copy annually’ is doubling in size every two years from 2013 to 2020 according to the research conducted by IDC. Yes, we should be data savvy managers as Machine learning stands by us!