Machine learning might be India’s next big employer

On professional sites, eggheads are discussing how technology such as machine learning or ML has impacted businesses and the prospects of career growth in India.

Some others are racking their brains, trying to dissect how machine learning is going to shape the employment scenario in India. The point is that it will certainly impact the employment scenario; the only debate is about just how much and to what extent?

Not unnaturally, machine learning is currently a topic of discussion among industry leaders, students, and in academia. The impact is such that ML is consistently in the list of LinkedIn’s top emerging jobs every year. 

This has increased the demand for machine learning-specific courses. Keeping pace with such a demand, academia has responded by incorporating this subject into curricula. They have also introduced specific short-term programmes in this dynamic field.  

However, it remains to be seen how the job market will react to these great expectations. Let’s begin by understanding what machine learning is all about in the first place. It is a subset of Big Data Analytics. It refers to the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. 

ML is seen as a subset of Artificial Intelligence (AI) and an enabler for Big Data Analytics. Applications of machine learning and deep learning range from computer vision to speech recognition and translation to marketing to drug discovery. It is one of the fastest growing fields of AI.

This particular field is also growing because of the blurring of lines between the digital and physical worlds, creating new opportunities for digital businesses. The digital world is increasingly, becoming a reflection of the physical world as the latter produces huge volumes of data through digitally-enabled ecosystems.

According to NASSCOM, Big Data Analytics application is poised for exponential growth in India. It is expected to witness an eight-fold growth by 2025, to cross $16 billion from the current $2 billion.  NASSCOM is working towards placing India among the top three markets globally in the next three years. Specifically, with regard to machine learning, NASSCOM expects that with increased adoption of futuristic technologies such as AI and ML, the Cloud market in India will grow to $7.1 billion by 2022. 

The growth prospects are likely to be further bolstered by India’s already strong position in software as a service (SaaS) segment, a booming e-commerce sector, Blockchain adoption and the growing focus on data security.

The specific job roles that will emerge are those of data scientists, data analysts, data system developers and functional analysts. ITES, manufacturing, finance, retail and healthcare sectors will offer the highest recruitment potential.  

Yet, if we examine the deployment level, training in analytics is still a nascent field in India. Currently, training is primarily imparted in specific tools (short term, 2-6 months) and through PG certification/diploma (medium term, 9-12 months). 

The current emphasis is on training in technology. In response to the need for bridging the gap between technology and management, some institutes have started offering two-year PGDM in Business Analytics (BA).

If the academia wants to meet this genuine talent need from the industry, then it needs to gear up for several challenges. The first comes from being able to hire an expert and experienced faculty members, which is expected to be in short supply, given that the field is in its early stages of development in India. Here, associating with visiting faculty members can be a reasonable thought, until a stable pool is locally created. 

The second would be creating the required infrastructure, including world-class labs for practical work, high-capacity servers and superior bandwidth. A third would be tie-ups with reputed global institutions that can help to bring in quality into course work and pedagogy. 

Lastly, institutions would do well to collaborate with the industry to structure and perhaps even deliver a curriculum since, practitioners have already made significant headway in this dynamic field. Focussing on quality at this stage will help us avoid the problems that plague both engineering and management — the issue of quantity over quality. 

The author is director, FORE School of Management