What are the challenges of machine learning in big data analytics?

Machine Learning is a branch of computer science, a field of Artificial Intelligence. It is a data analysis method that further helps automate the building of the analytical model. Alternatively, as the word suggests, it gives machines (computer systems) the ability to learn from data, without outside help to make decisions with minimal human interference. With the evolution of new technologies, machine learning has changed a lot in recent years.

Let’s discuss what is Big Data?

Big data means too much information and analytics means analyzing a large amount of data to filter the information. A human cannot do this task efficiently within a time limit. This is the point where machine learning for big data analytics comes into play. Let’s take an example, suppose you are a business owner and you need to collect a lot of information, which is very difficult on your own. Then you start to find a clue that will help you in your business or make decisions faster. Here you realize that you are dealing with immense information. Your analytics need a little help to make the search successful. In the machine learning process, the more data you provide to the system, the more the system can learn from it and return all the information you were looking for and thus make your search successful. That’s why it works so well with big data analytics. Without big data, it cannot function at its optimal level due to the fact that with less data, the system has few examples to learn from. So we can say that big data has an important role in machine learning.

Instead of several advantages of machine learning in analytics, there are also several challenges. Let’s discuss them one by one:

• Learning from big data: With the advancement of technology, the amount of data we process is increasing day by day. In November 2017, it was discovered that Google processes approx. 25PB per day, over time, companies will cross these petabytes of data. The main attribute of data is volume. Therefore, it is a great challenge to process such a large amount of information. To overcome this challenge, distributed frameworks with parallel computing should be preferred.

• Learning different types of data: Today there is a wide variety of data. Variety is also an important attribute of big data. Structured, unstructured, and semi-structured are three different types of data that result in the generation of high-dimensional, nonlinear, and heterogeneous data. Learning from such a large data set is challenging and results in an increase in data complexity. To overcome this challenge, data integration must be used.

• High Speed ​​Streamed Data Learning: There are several tasks that include completion of work in a certain period of time. Speed ​​is also one of the main attributes of big data. If the task is not completed in a specific amount of time, the processing results may become less valuable or even useless as well. For this, you can take the example of stock market prediction, earthquake prediction, etc. Therefore, it is a very necessary and challenging task to process the big data on time. To overcome this challenge, the online learning approach must be used.

• Learning of ambiguous and incomplete data: Previously, machine learning algorithms provided relatively more accurate data. So the results were also accurate at that time. But today, there is ambiguity in the data because the data is generated from different sources that are also uncertain and incomplete. So it’s a big challenge for machine learning in big data analytics. An example of uncertain data is the data that is generated in wireless networks due to noise, shadows, fading, etc. To overcome this challenge, a distribution-based approach must be used.

• Low value density data learning: The main goal of machine learning for big data analytics is to extract useful insights from large amounts of data for business benefits. Value is one of the main data attributes. Finding significant value from large volumes of data that have a low value density is very challenging. So it’s a big challenge for machine learning in big data analytics. To overcome this challenge, data mining and knowledge discovery technologies in databases must be used.