How to Solve the Shortage of Machine Learning Engineers?

The surge in the demand for machine learning professionals and data scientists is clearly very high in this fast-moving technology environment as researched by the GSDC community. LinkedIn recently posted an insightful report that sheds light on the significant shortage of machine learning engineers needed, forcing companies to invest heavily in the internal training and program development of professionals. 

This gap is driven by technological advancements, which are creating a demand for more data-related roles that are currently outpacing the supply of qualified professionals. 

Current Business Landscape

The Demand Surge

According to the Bureau of Labor Statistics, the demand for machine learning jobs will increase by 23% from 2022 to 2032. It is comparatively higher than any other profession. We can see this growth in the challenges corporations face when recruiting for data scientist roles where many hiring managers need help to fill the positions. 

The Skill Gap

Many candidates competing for the role of data scientist lack the skills needed for this complex job profile. Many data science experts have advanced degrees, and there is a skills shortage in these areas due to the surging demand. For example, the UK alone requires 215,000 personnel with hard data skills but the universities only provide around 10,000 graduates annually who know data science. 

High Compensation

The talent competition is heightened by the profitable nature of data science employment, with experienced workers earning median salaries of approximately $150,000. Better offers from competitors frequently prevent businesses from keeping talented data scientists on staff.

How To Address This Challenge Of Shortage Of Machine Learning Engineers

Leading businesses are implementing many new strategies to mitigate this shortage of machine learning engineers. 

Training and Development

To fill the machine learning positions, organizations are investing in training current staff members. Identifying potential candidates and giving them the required training in machine learning and data analytics are part of this process.

Career Switching

Numerous experts from diverse domains are seeking to shift into the field of data science. Businesses can take advantage of this talent pool by providing training programs that enable these people to gain the much-needed skills primarily required for these roles.

Remote Work Opportunities 

Businesses may identify competent candidates by exploring beyond their regional marketplace. While hiring data scientists from areas with a larger talent pool is made possible by providing remote work arrangements, data security issues are brought up by this method. However, this challenge can be overcome in different ways. You can sit in a different country and hire an employee from a completely different country. 

No-code Solutions

The use of no-code machine learning technologies helps empower data analysis within enterprises by allowing non-specialists to carry out fundamental analytics tasks. This can increase the number of people who can interact with data and lessen the strain on data scientists.

Industries Response To This Shortage

Companies are investing more money in training and upskilling programs to develop their talent in response to the shortage of skilled professionals. A Deloitte poll from 2023 revealed that 65% of companies said they had increased funding for training efforts, including AI and machine learning in the previous year. Major training programs are being offered by companies such as Google, Microsoft, and IBM to assist their staff in acquiring the skills required for these positions. Certification like GSDC’s  ‘Certified Machine Learning Professional (CMLP) is helpful for training. 

Additionally, a growing number of online learning environments are available with specific machine learning and artificial intelligence courses. AI and machine learning course enrollments on platforms such as Coursera, edX, and Udacity have increased significantly. Coursera, for example, revealed that enrolments in AI-related courses increased by 60% in 2023 over the prior years. 

Bootcamps and short-term certification programs that help professionals acquire relevant machine learning skills are becoming increasingly popular. These programs give individuals the skills and methods they need to be successful in the profession through real-world experiential learning. 

Takeaway

Given the pace of technology development and the slow rate of skill education, there will probably be a shortage of machine learning engineers for some time to come. Nonetheless, it is anticipated that businesses, academic institutions, and governments working together will eventually close the gap. 

Businesses must actively work on this challenge if they want a successful outcome in the near future. They must also invest in continuous learning and encourage an innovative culture to create a resilient workforce that can use AI and machine learning to drive future growth. 

In conclusion, the shortage of skilled machine learning engineers is considered a serious issue, but it also presents a chance for future development and creativity. By promoting training, education, and collaboration, we can ensure that the workforce is ready for the future and that the advantages of AI and machine learning are felt across industries.