Why Organizations are Entering The Age of Automated Machine Learning in 2020?

by James Warner 7 months ago in future

Whatever be the need of the hour for technological companies around the world, putting some data into machine learning algorithms is one of the smartest and effective ways of solving a problem.

Why Organizations are Entering The Age of Automated Machine Learning in 2020?

Machine learning might not be a much-diversified field in the world today, but whatever amount of space and time that it occupies is important. It is a critical tool for researchers and scientists and also for organizations that are either carving a niche for themselves in the market or looking for ways to establish their competitive dominance. Whatever be the need of the hour for technological companies around the world, putting some data into machine learning algorithms is one of the smartest and effective ways of solving a problem.

Machine Learning in Organizations

Digital transformations are becoming more and more fundamental to organizations. This is yet another way of enunciating that if one doesn’t sail with the tides of the digital, they might get lost in the farfetched race of the market. Digital transformation has not just become a necessity now but also a factor that gives you a clear picture of your customers in the market.

Since the wants and preferences of the customers appeal as infinite to organizations practicing traditional business values, digitization can be the perfect shift from the mundane. But it’s not just the digital transformation that is lifting the organizations. Instead, it is the facilities of other technologies like machine learning and artificial intelligence that push organizations and enterprises to discover more about the market and prepare themselves for it.

As time flies by, a majority of organizations, regardless of their industry and niche are getting on board with this idea. They want to be able to uncover all that’s there in the data and use it to make an impact with their customers. Since data is abundant and every business has more than enough of it, it all comes down to how well they can derive insights from it and use it to back their decision making.

For most organizations, predictive and prescriptive analysis is the way going forward and understanding the many folds of the customer behavior concerning the market. While several businesses are already there and using it for hardcore data-backed decision making, it is ultimately the small and medium enterprises that are burdened with accomplishing a lot with a limited number of resources on their plate.

When it comes to deriving insights, most businesses are turning to data science for the task. While the field has answers to all questions of the business, it is also one of the toughest tasks out there. The point is that data science is a complex task requiring the knowledge and analysis of more than a few fields such as statistics, mathematics, programming, machine learning among others. This makes it difficult to find the right resource, which would be well versed in these technologies along with having hands-on experience in its functioning.

So, the ultimate choice comes down between settling for mediocre methods to carry out traditional business processes and investing in the field of data science. Even if organizations do the latter, there is no guarantee that they will find someone suitable that understands the needs of the organizations and caters to that of data using their skills.

Therefore, the underlying questions for organizations remain, what do they do in times like these, we’re even planning to invest in a specific resource doesn’t guarantee the expected results. Having said this, organizations and enterprises from all over the world are entering the age of automated machine learning solutions.

Automated Machine Learning

The lack of highly qualified data scientists is evident considering the increased demand for advanced predictive and prescriptive analysis by organizations all over the world. But since data science has become an indispensable part of doing business, organizations are finding a coping mechanism in what is called the ‘citizen data scientist’.

Not only this trend help to close the skill gap but also efficiently cater to the data science needs of an organization. But, organizations can’t think of it as a direct replacement, since citizen data science lacks several aspects of the advanced data science domain. Instead, the new field demonstrated its potential in generating models backed by state of the art diagnostics along with predictive analysis. The direct ability of this new filed can be attributed to the emergence of technologies like automated machine learning.

Popularly known as AutoML, the cutting edge technology aims to automate the end to end processes of applying ML to real-world problems. In other words, it automated the algorithms of other ML models. If you consider the standard ML pipeline, it is made up of the following- data pre-processing, feature selection, feature engineering, selection go algorithm along with tuning of hyperparameters. However, accomplishing all of this takes a lot of time and poses an entry barrier for people trying to venture into the field.

The underlying idea of AutoML, on the other hand, removes all of this and tries to democratize machine learning. It significantly reduces the time taken by an ML algorithm to transit through its pipeline, also without the intervention of human supervision. More to this, research by the Harvard Business Review suggests that AutoML has the potential to improve the accuracy of the model, in comparison to the handcrafted models that are explicitly programmed by humans.


AutoML aims at helping all those businesses who do not want to be limited by the constraint of a data science resource. This can not only help them do more with limited expertise in the field but also give them more accurate and relevant results to problems they intend to solve.

James Warner
James Warner
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James Warner

James Warner is a Business Analyst / Business Intelligence Analyst as well as experienced programming and Software Developer with Excellent knowledge on Hadoop/Big data analysis, testing and deployment of software systems at NexSoftSys.

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