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How Machine Learning is Transforming the Indian IT IndustryCheck with seller

Mumbai, Maharashtra - India

Date of Publication:
2021-10-11 21:41:59

Contact Data:
Sana Khan

Phone: 18002705400

Description: As technology embeds itself into every aspect of our lives, machine learning has emerged at the centre of India’s digital transformation. From the use of virtual personal assistants in everyday life to the adoption of image recognition software, technologies driven by machine learning are deeply embedded in our daily lives. Machine Learning is being used for a wide range of functions within the business domain, including market research, search engine optimisation, traffic forecast, customer service, data management, risk mitigation, big data analytics, AI algorithms, and fraud detection. In fact, the market for machine learning is expected to grow to $30.6 billion by 2024 at a Compound Annual Growth Rate (CAGR) of 42.8%. In India, the highest investments among the various AI technologies are in the field of machine learning applications. The reason behind these high investments is the growing popularity of ML applications across business functions like finance, accounting, general management, IT, operations, and administration. In fact, NITI Aayog has noted that India can add 1.3% to its GDP annually through machine learning and artificial intelligence. Opportunities and Challenges For Aspiring ML Professionals An increase in the applications of machine learning across several fields and increasing investments have led to an ever-rising demand for machine learning experts. For instance, the job title ‘Machine Learning Engineer’ topped LinkedIn’s rankings for Emerging Jobs between 2012 and 2017, with a growth rate of 9.8. However, the increasing demand for jobs in machine learning is faced with a shortage of talent. Machine learning is still a novel field in India, and as such, there is a severe shortage of upskilled professionals within this domain. A recent survey by O’Reilly Media has shown that the shortage has subsequently slowed down AI adoption in enterprises. The reason for the shortage of talent can be attributed to two causes. Firstly, companies hiring machine learning programmers are often biased towards the academic pedigree of the job applicant. Programmers aiming to opt for machine learning jobs often find that the jobs have entry barriers that are biased towards students from prestigious universities like IITs. Secondly, most programmers lack an in-depth knowledge of machine learning that can be applied within business contexts. They lack the industry training and specific skillsets required to succeed in corporate roles. To know more: