Mandatory in-demand skills to start career in AI/ML

Data is only useful after proper data modelling techniques have been applied to give it a definitive structure, from which key insights can be drawn.

Mandatory in-demand skills to start career in AI/ML
AI requires the use of data modelling to recognise these patterns and relations.

By Meghan Nandgaonkar

It is the era of Artificial Intelligence (AI) and Machine learning (ML). AI is considered one of the most revolutionary technological developments of our time. Beyond technology giants and other major industries, AI is also woven into our everyday lives through gadgets, social media, smart home devices and banking, among other aspects. The day is not far when AI and ML-enabled machines will impact all spheres of our lives. 

Artificial Intelligence is about building computer systems that can simulate human intelligence. It helps a computer system perform tasks and make decisions or choices independent of human intervention. Related technologies, like machine learning, cover the techniques that enable systems to develop the needed intelligence. With ML, the systems learn from past data or experiences, without being explicitly programmed to do so. As the field of AI and ML keeps evolving, it will continue to offer a plethora of job opportunities. It is estimated that India could see AI churn out nine lakh white-collar jobs and thirty-six lakh indirect jobs by 2030.

For anyone aspiring to make a career in AI and ML, here are eight in-demand skills one needs to possess:

Mathematics: Good mathematical skills are essential for all AI/ML professionals as they regularly deal with applied mathematics and algorithms. AI professionals are also required to decode huge datasets using mathematical methods. Strong mathematical skills combined with analytical thinking help technology professionals in solving complex AI problems. Aspirants are required to be proficient in statistical or mathematical concepts, such as optimization techniques, graphs, probability, linear algebra, calculus, or matrices etc., to be good at AI/ML programming.

Data Modelling: Data is at the core of AI and ML as all the programs need tons of data to run on. More the data, the better the results. But the huge set of information can be leveraged after it is properly classified, to support data analytics. Reviewing the data and understanding the key relationships between data points is a necessity. Data is only useful after proper data modelling techniques have been applied to give it a definitive structure, from which key insights can be drawn. AI requires the use of data modelling to recognise these patterns and relations. Companies can model the information as per their requirement through data modelling and simplify the process of AI/ML-driven analytics to obtain appropriate solutions to their business problems.

System Design and Architecture: An ecosystem of products is created through a combination of machines brought together. It is important to understand how each piece fits in the puzzle and how they communicate with each other. System design or architecture defines the quality attributes of the program being built. Any AI/ML-based system developed without the appropriate architecture will lead to failure due to the system’s behaviour on data, dependencies on shared resources and misaligned environments for various components. Hence, it becomes an absolute necessity to have the requisite skills in system architecture to develop a smooth-running and desirable AI/ML system.

Natural Language Processing (NLP) and Neural Networks: AI/ML systems aim to replicate human intelligence and decision-making. Hence, the system will be incomplete without the ability of computers to understand how humans communicate and how we process information. Neural networks are made to duplicate the way a human brain functions. NLP in AI/ML systems brings immense capabilities, getting them closer to how humans communicate through various languages. Individuals can get a head start by knowing the fundamentals of NLP and neural networks. Since AI professionals are required to design neural networks, it is imperative they understand the system. This will help them bring innovation and creativity to their work.

Programming languages: It is fundamental to learn the languages that computers and data deal in. Programming languages help computers communicate with various systems. Thus, aspirants must know the programming languages used in AI applications like shell scripting, C++, Java, R and Python, among others.

Domain Understanding: With AI and ML, a good understanding of the industry domain is crucial. One should know the challenges they are trying to address or why there is a need for an AI and ML solution, or else the efforts may not be in the right direction. It is essential that AI professionals engage with the right stakeholders to understand the business environment and the specific problems that they are required to solve. Better knowledge of the domain will go a long way in helping professionals frame the right architecture & systems. Proper domain knowledge also helps in anticipating future challenges. Learning and honing advanced technological skills requires grit and patience. One must keep up by always being updated with the latest technological advancements.

Critical thinking: In addition to having domain knowledge, it is important to have critical thinking & analytical skills. This will help AI professionals to dissect problems into smaller pieces and approach the best solutions in a simplified manner. It is important to be curious, ask the right questions and make decisions based on trial and error with respect to AI and ML. Critical and analytical thinking skills will aid in producing solutions to address even the most complex business problems.

Communication skills: An AI and ML engineer does not work alone. They will also be required to be good collaborators and work with other engineers or non-technical divisions like marketing or sales departments. AI & ML professionals focus on solving business problems; hence they are required to coordinate with different teams in the organisation to understand the problems better and create appropriate solutions. Proper communication skills will also help AI professionals to transmit their ideas seamlessly and facilitate smooth functioning between teams.

Without a doubt, AI & ML jobs will continue to be in demand in the future. Therefore, it is important that aspirants invest their time & energy in upskilling, so they are future-ready for a successful career in AI & ML. They must also keep themselves updated with all the latest developments in the space and conversations surrounding it. Apart from mastering these eight skills, it is just as crucial to enjoy the world of technology. One must be driven by the various problems they can help solve and the eventual benefit it can bring to individuals or society.

The author is head of JDU, Global Delivery, Fujitsu.

Also Read: No plans to conduct SSC exams only in Hindi, Centre tells Rajya Sabha

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First published on: 08-12-2022 at 08:00:00 am