Today, AI, machine learning, and big data have become all-pervasive, with industries across the board exploring these technologies to become more competitive. When used well, these technologies can potentially transform how businesses are run. According to Gartner, AI implementation has jumped by a massive 270% in the past four years. In fact, there has been a growth of 37% just in the last year.
Needless to say, this stupendous growth has also had an impact on the job market. We can expect to see a massive churn in the job market as AI automates several jobs and in turn, creates new ones. We can expect AI to create 2.3 million jobs by 2020 as per Gartner. At the same time, it will eliminate about 1.8 million jobs.
So, while the absolute number of jobs may increase, the skills required to perform and flourish in these new roles may be quite different from current skills that engineers possess. Therefore, it makes sense for engineers to imbibe the right skills in AI and data can help provide a boost to their careers. This will also enable them to add greater value to their organizations, irrespective of their specific area of work.
In general, here are some skills that every engineer must imbibe in the age of AI and data.
Basic programming and knowledge of computer science
Besides, a solid understanding of some of the basic software development concepts such as the traditional Waterfall approach as well as the Agile methodology goes a long way. They should also have some insights into how programming can help address business issues. Also, considering that most of the services today run on the cloud and companies across industries are adopting cloud, thorough knowledge of cloud computing is a huge plus.
They say that in today’s age, data is the new oil. The ability to collate the right data and use it to gain useful insights can prove to be the biggest differentiator for any organization. Therefore, it is crucial for engineers to understand how data behaves and how it can be modelled to gain actionable insights. Data is especially important since it forms the backbone of any machine learning algorithm. The ability to integrate various information systems, design useful databases, and use them to mine data efficiently provides an edge.
Knowledge of statistics
Given that most AI and machine learning algorithms are based on mathematical and statistical concepts, knowledge of statistics can prove to be useful. A flair for mathematical problem solving and good analytical skills come in handy for any AI/ML programming. In addition, engineers should also hone their skills in statistics and probability to help them understand the field of AI/ML better.
Ability to think beyond technology
When it comes to new concepts such as AI/ML, technology is only half the story. The ability to look at the big picture and understand how AI and ML can be leveraged in business is equally crucial. This means taking in disconnected bits of data and making sense of them in the context of your business, which requires basic domain and business understanding. Also, strong storytelling skills to help make business sense out of available data and articulate the same to the stakeholders can be an asset. Additionally, an innate curiosity, patience, ability to reason, and arrive at logical conclusions are very important qualities.
Keeping pace with AI/ ML developments
Machine learning and artificial intelligence are rapidly growing fields. Every single day, there is a discovery or new use-case which has the potential to disrupt an industry. Ensuring that you are constantly in touch with new developments and understand their potential is vital to stay current.
Today, it makes sense for engineers to prepare themselves with requisite skills irrespective of whether they are freshers or part of the industry. Taking a structured approach to developing these skills is important than taking random MOOCs (Massive Open Online Courses) that teach a new programming language or two. It is useful to do certifications in subjects such as data science, machine learning, deep learning, Python, etc. While choosing a course to further your AI skills, it makes sense to opt for a blended learning approach – with a mix of online self-learning and interactive, immersive sessions.
As an engineer, the well-rounded knowledge of AI/ML is a requisite, rather than a luxury today. Investments like such are useful to build a long a successful engineering career.
(Author Anand Narayanan is Chief Product Officer, Simplilearn. Views expressed here are personal.)
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