What Are the Challenges of Machine Learning in Big Data Analytics?

Machine Learning is a field of computer science that belongs to Artificial Intelligence. It is an approach of data analysis which further helps automate the process of making analytical model. On the other side, it enables machines to learn from the data, without additional help to make decisions with least human intervention. Thanks to the arrival of modern technologies, machine learning has seen a lot of changes over the past couple of years.

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What is Big Data?

Big data refers to a whole bunch of information and analytics refers to analysis of large data to filter out the details. A human can do it well in the specific time frame. There are times when you need machine learning for big data. Suppose you own a company and you have to collect a lot of information, which is very hard in itself. Then you can find a clue which can help you make decisions quickly in your business.

In this process, you can realize that you are dealing with whole lot of data. Your analytics need some help to make search more effective. In the process of machine learning, the more the data provided to the system, the more the computer learns from it. Then, the system can learn more from it and return all the details you were looking for and your search process can be easier.

This is why it is so compatible with big data analytics. It cannot work well without big data. Due to the lack of data, there are few examples for you to learn from. So, it goes without saying that big data is always important for machine learning. Instead of different benefits of machine learning in big data analytics, there are different challenges to look for.

Huge Data to learn from – Thanks to the technological advances, there is huge amount of data to be processed every day. Google processed around 25 petabytes (PB) of data per day in 2017 and companies are looking forward to cross this limit in future. Volume is the major attribute behind the data. So, it is not so easy to process such a huge amount of data. Distributed frameworks are preferred with parallel computing to deal with this challenge.

Learning different types of data – There is a huge chunk of data you can find these days. mixture plays a very important Role in big data. There are three types of data you can work on – structured, semi-structured and unstructured, which help in generating non-linear, heterogeneous and high dimensional data. It is not easy to learn from such a great set of data and it further increases complexity of data. Data integration is vital to deal with this hurdle.

Learning Incomplete/Ambiguous data – Machine learning algorithms once provided more accurate and relevant data. Hence, we have also got more accurate results back then. These days, there is huge chunk of data as it is generated from various sources which are both incomplete and uncertain. So, it is not easy for machine learning in big data. Uncertain data usually refers to the data generated due to shadowing, noise, and fading in wireless networks. So, it is vital to use distribution based approach to deal with this challenge.

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Learn streamed information in high speed – There are different tasks you need to complete over a certain time period. Velocity is one of the major players in big data analytics. If you are failed to finish the task in the given time period, outcomes may be less valuable or even won’t worth anything at all. So, you can learn from earthquake prediction or stock market prediction processes. It is a very challenging and vital task to process big data in timely manner. To deal with such challenges, you need to use the approach of online learning.

Learning data with low-value density – The main purpose behind machine learning is extracting all the vital information for big data analytics from huge chunk of data for commercial purposes. Value is among the major data attributes. To find great value from such a huge volume, it is not easy to have low-value data. For machine learning, it is a major hurdle. You need to use your knowledge and data mining technologies in databases.