From education and healthcare to finance and transportation, these disciplines are transforming how work is perceived. | iStock/Getty Images

The unprecedented development of digital technologies highlights the norm of global growth in the 21st century. Tremendous advances have been made in numerous digital technologies, such as Artificial Intelligence (AI), and supporting disciplines like Machine Learning, Data Engineering, and aspects of Deep Learning have emerged as a result.

This digital technological revolution has put data at the centre of attention, and the science of analysis, engineering, and production -- all of it has been put together to create an ecosystem that is being used by corporations and individuals alike, highlighting the mass demand. This demand extends far beyond business and organizational agility, into daily usage by individuals, and the growing demand has put immense focus on democratising relevant roles to the mass workforce.

To put this into perspective, Machine Learning as a tool is being increasingly used in pattern recognition, user predictions, automation, personalisation, and more in industries like e-commerce, entertainment, navigation, healthcare, social media, transportation & logistics, finance, and many more. The global ML market that was valued at $56 billion in 2024 is now estimated to increase to $282.13 billion by 2030, an annualised growth of 30.4%.

Deep Learning has been one of the major catalysts behind this immense scalability of the ML market globally, and other verticals like Data Engineering are extending the emphasis on this growth. At this point, these fields require mass participation to align with the demands, meaning individuals with PhDs are only becoming the spearhead to the research & development aspect, while widespread operations are being pushed through by trained professionals.

Democratisation of skills

What makes a technology truly viable is widespread adoption. While widespread adoption is no longer a challenge owing to the significant operational agility of thousands of businesses, aligning with the demand is turning out to be a challenge in itself. Disciplines like Machine Learning, Data Engineering, and Deep Learning are considered premium skills at the moment, especially because only a fraction of today's professionals are trained in them. For the majority, they are either upskilling in these disciplines or are learning it from the very beginning. However, the process has begun to democratise these skills to the mass workforce, so present and future demands can be catered to in a streamlined manner.

The democratisation of new-age skills in Machine Learning, Data Engineering, and Deep Learning not only highlights the demand but also how they are becoming an integral part of how we operate as a civilisation. From education and healthcare to finance and transportation, these disciplines are entirely transforming how work is perceived.

The true extent of the allocation of these disciplines is so vast that it is impacting millions of individuals every day, while the vast majority of them are yet to understand it. This large-scale deployment in a sector agnostic role requires a dedicated workforce that are able to not only work with it, but for it, and individuals with PhDs are required to future-proof it currently, not oversee the deployment and day-to-day operations.

Transformative roles in tech

While it is understandable that individuals with PhDs are required for developing these disciplines, the democratisation of the larger operation also highlights the transformation in tech employment roles. Open source tools and online databases are assisting new-age developers to learn and build complex algorithms.

They are also receiving help from frameworks such as TensorFlow, PyTorch, and Scikit-learn with codebases, empowering them to understand the foundations of Machine Learning, and even to highly sophisticated neural networks. The tech role as an impact is becoming more aligned with data preparation and model deployment, aspects that are being taken care of by trained professionals while leaving the mathematical and computational theory aspect to individuals with PhDs.

The shift in sectoral roles becomes much more clearer when the hiring trends are analysed. At present, corporations are more interested in widespread deployment and sustenance. For this, the roles in the IT sector are increasingly witnessing openings in Data Analysts, Data Scientists, and ML engineers.

Professionals in these roles spearhead the deployment and sustenance aspect, working in roles that focus on data cleaning, pipeline development, and model deployment using predetermined technologies. The emphasis here remains on the usage of the tools themselves, not on how to build them in the first place.

How does the future look?

At present, the focus remains on upskilling as many professionals as possible to create and sustain an ecosystem that is thriving on digital technologies like ML, Data Engineering, and Deep Learning. However, the roles of researchers, especially individuals with PhDs, will be important to develop newer aspects, as well as to create new technologies that will not only supplement but also overtake the existing technologies.

For now, deployment of these technologies remains at a very nascent stage, and we can expect them to reach the pinnacle of their potential in the coming years with true democratisation of these skills and related tools.

(The author is Arun Prakash M, Founder & CEO of HCL GUVI)

Deeptech for common people: How ML, Deep Learning, and Data Engineering are shaping tech careers