Use of Big Data in Machine Learning

The use of big datasets in machine learning has grown exponentially since the first release of Apache Hadoop. Big Data has played a major role in the use of machine learning in applications such as mass clustering and collaborative filtering. In this article, I’ll walk you through the use of big data in machine learning and when you should use big datasets to solve machine learning problems.

Use of Big Data in Machine Learning

Imagine an online shopping platform with a user base of 1 million with just 100 products. Consider a matrix where each user is associated with each product by implicit or explicit classifications. This matrix will contain 1000,000 x 1000 cells and since the number of products is much less than the number of users, it will take a long time for each user as it will slow down the entire process of each transaction.

Now imagine training a machine learning model with over a million samples. A single sample is iterated so many times. If such a problem can be solved using traditional approaches, it will not be surprising for me to say that we have to wait for the next few days for our machine learning model to be trained and to work well.

But if we use a big data approach on these kinds of problems, we can train the machine learning models at the same time that we take while training models in our local machine with smaller datasets.

When do We need To Use Big Data in Machine Learning?

Not all machine learning problems are suitable for big datasets, and not all big datasets are useful for training machine learning models. However, their conjunction in particular situations can lead to extraordinary results while retaining many limitations that often affect smaller scenarios.

A data scientist or machine learning engineer needs to understand when the big data approach is really useful and when the burden may outweigh the benefits of using big datasets to solve a particular problem. So, using big data to solve a machine learning problem is necessary when:

  1. The dataset cannot fit in the memory of your high-end system.
  2. The incoming data flow is huge, continuous, and requires quick calculations.
  3. It is not possible to divide the data into smaller parts.


I hope you now have understood the use of big datasets in machine learning and when you should use a big data approach while solving a machine learning problem. I hope you liked this article on what is the use of big data in machine learning. feel free to ask your valuable questions in the comments section below.

Aman Kharwal
Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of data📈.

Articles: 1500

One comment

Leave a Reply