Four Crucial Skills for Machine Learning and Artificial Intelligence – EnterpriseTalk

  • Lauren
  • September 14, 2021
  • Comments Off on Four Crucial Skills for Machine Learning and Artificial Intelligence – EnterpriseTalk

To increase their value in the fast-growing AI field, top Artificial Intelligence professionals will need to develop a few key skills that go beyond just technical expertise.
According to ‘LinkedIn Jobs on the Rise: 15 opportunities that are in demand and hiring now’, artificial intelligence (AI) is one of the fastest-growing occupations, with practitioners in great demand in 2021. The best AI/ML professionals and teams are well-rounded in their broad business understanding and ability to communicate, in addition to having expertise in Python, C++, or Java and an aptitude for math.
The next step of digital transformation is organization-wide adoption of AI/ML technologies; therefore a strong team of developers, programmers, and data scientists is essential for enhancing AI literacy from the top down. It is critical for IT leaders to emphasize that AI/ML is intended to improve, not completely replace the organization’s teams.
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Continuous Learning
One of the most powerful non-technical skills that AI/ML teams can employ is one they almost certainly already have: a natural interest about the challenges they are working on and a creative approach to tackling them. These skills will come in handy when it comes to leading the implementation of AI/ML technologies within the company.
In addition to being a leader in AI/ML implementation, the team must understand the ever-evolving technology themselves. When it comes to expanding their staff, CIOs should search for people who can think on their feet and adapt quickly to new ideas. In 2021, the pace of innovation will not decrease, and it will strengthen the company’s ability to develop a workforce of natural learners.
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Furthermore, low-code/no-code industry solutions that enable citizen developers to streamline their workflow with the touch of a button are becoming more popular. Even the most experienced engineers may soon be forced to adapt to no-code platforms, so building a team that can think on its feet is critical.
The ability to communicate the value of data
Though a thorough understanding of the technology is important to the success of AI/ML teams, the ability to explain the value of the data in a non-technical manner is what distinguishes the all-stars from the average players.
Are the teams using their knowledge of the technology and business concepts to evaluate the data, draw conclusions, and make helpful recommendations? The best teams can translate technical jargon into terms that non-data teams can comprehend without losing the integrity of the principles.
Enthusiasm and excitement
Excitement and enthusiasm are sometimes ignored when discussing extremely detailed technical roles, despite the fact that they are straightforward and seemingly obvious. Both, however, are important to the organization’s growth.
During times of stress and uncertainty, enthusiasm and excitement transform into resiliency, which aids in the advancement of innovation. Look for ways to bring in these kinds of individuals at all levels of the organisation
Understanding the social ramifications of AI
It’s easy to get tangled up in the jargon of AI/ML development and implementation. An outstanding data practitioner, on the other hand, will look past the jargon to see the bigger global implications of new technologies.
In the midst of ethical worries about deep fakes and prejudice in AI systems, it’s critical that teams stay engaged in the dialogue. Cultivating ethical corporate leaders who aim to see the work’s global impact could save CIOs from worse difficulties down the road – and possibly even put the company ahead of the competition in the public view.
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