The Growing Machine Learning Talent Gap and how to Bridge it


Today business enterprises are trying to bring machine learning into their arsenal, but without the right skills they will likely fail. The number of jobs that machine learning could make redundant over the next few decades is a growing cause of concern amongst many of us.  According to a research study conducted by the PwC, 38% of jobs in the United States will be automated by the end of 2030, while in other parts of the world it would be somewhat less. In the United Kingdom, it will be 30%, and in Germany, 35%. While it can’t be denied that hundreds of thousands of jobs will be lost, as with all periods of technological advancement, we will witness the creation of new jobs.

Many of these new jobs will be dedicated to developing and supervising machine learning algorithms, helping business enterprises to incorporate and implement the technology and bring in efficiencies in their business operations. To some extent this has already begun.  According to Indeed.com, a popular job search website, from June 2015 to June 2017 there was a 500% rise in the job openings in the field of artificial intelligence (AI). Of these job postings listed on the Indeed website, 61% of the jobs in the artificial intelligence (AI) industry were for machine learning engineers, while 10% of the jobs were for data scientists and only 3% were for software developers.

However, machine learning is going through the same problem STEM has suffered from since the dawn of time: A lack of skilled people to fully leverage the potential of machine learning. There is a shortage of qualified professionals who understand where it is appropriate to apply, and secondly, how to apply it to fully exploit its potential. If the reports of a survey from Tech Pro Research are to be believed, only 28% organizations have some experience with artificial intelligence or machine learning, while more than 40% said that their organization IT staff don’t have the right skill sets to implement and support artificial intelligence and machine learning.

The best solution to bridge the machine learning skills gap in an organization is organizing corporate training programs. To address the widening talent gap in machine learning, businesses should start considering corporate training in machine learning and artificial intelligence. By doing this, you will not only upskill your existing employees, but also create a loyal workforce for your organization. This is how the talent gap can be bridged by enterprises.
If you also know some other ways to solve the machine learning skills gap in an organization, kindly let us know in the comment section below.

Location: Bengaluru, Karnataka, India

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