Today, big data is streaming into businesses at a tremendous rate unlike the past when tech geeks and scientists mostly used machine learning. Machine learning is the most significant frontier in big data innovation, but it is daunting for people who are not data science domain experts or geeks.

However, several tech giant organizations are now making it easy for developers to employ machine learning to a dataset so as to supplement predictive features to their applications. In this age of big data, there are various challenges facing machine learning which include:

Truth and Veracity

With the vast amount of information said in online platforms nowadays, it is very hard to distinguish what is genuine and what is not. Today there are robots intelligent enough to post content like humans beings. Machine learning has significant challenges to determine the truth or veracity of online information.


Today, there is a myriad of choices available online that are becoming difficult to settle on even a simple product such as a book. Machine learning will face a challenge in making smart recommendations based on user’s context and not just the preferences of the crowd, and this is so because it is imperative to understand the user’s context.

News Aggregation

There is plenty of news generated on a daily basis around us from all over the world. However, most people only thirst to consume the news relevant to them. This unlikeness is a challenge to machine learning since; it is hard to aggregate news according to the user’s preferences, the user’s taste vary with time, and it is critical to learning of these variations. Get in touch with us today for more information.



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