A career in machine learning: hype or real?

By: |
May 20, 2019 1:15 AM

Machine learning is a subset of a broad area known as Big Data Analytics.

Machine learning, LinkedIn, Big Data Analytics, artificial intelligence, NASSCOM, ITeS, cloud market The digital world is increasingly becoming a reflection of the physical world as the latter produces huge volumes of data through digitally-enabled ecosystems. (Reuters)

Machine learning (ML) has been garnering significant attention from industry, students and academia. ML is consistently in the list of LinkedIn’s top emerging jobs. This has resulted in a spike in demand for specific courses in ML. The academia has responded by incorporating ML into courses as well as coming up with specific short-term programmes. But will the job market live up to the expectations?

ML is a subset of a broad area known as Big Data Analytics. It refers to the scientific study of algorithms and statistical models that computer systems use to perform a task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence (AI) and an enabler for Big Data Analytics. Applications of ML 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.

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The growth is this field is driven by 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, the applications of Big Data Analytics will grow eight-fold by 2025, to $16 billion. On ML in particular, NASSCOM expects that with the increased adoption of technologies such as AI and ML, the cloud market in India will grow 3x to $7.1 billion by 2022. The growth prospects are likely to be bolstered by India’s strong position in the Software as a Service (SaaS) segment, booming e-commerce, blockchain adoption, and growing focus on data security.

The specific roles that would see demand are data scientists, data analysts, data system developers and functional analysts. Industries that can be expected to offer the highest recruitment potential are ITeS, manufacturing, finance, retail and healthcare.

Training in analytics is still a nascent field, despite all the noise we may hear. It’s imparted in specific tools (these are short-term, 2-6 month courses) and through PG certification/diploma (9-12 months). The 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).

To be able to supply enough talent, academia needs to gear up for challenges. The first is hiring expert faculty, which is expected to be in short supply. Tying up visiting faculty members would be crucial until a stable pool is locally created. The second is creating the required infrastructure, including world-class labs for practical work, high capacity servers, and superior bandwidth. The third is tie-ups with reputed global institutions that can help bring in quality into course work and pedagogy. Lastly, collaborate with industry to structure and perhaps even deliver curriculum, since practitioners have already made significant headway in this dynamic field. Focusing on quality at this stage will help us avoid the problems that plague both engineering and management education—that of quantity over quality.

The author is director, FORE School of Management, Delhi

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