Machine learning (ML) is the application of artificial intelligence (AI) for developing systems capability to learn automatically and improve their overall performance through experience, without being programmed explicitly. In India, ML is identified as an emerging technology and is adopted largely by retail, transportation, and financial services industries, among others. At present, there is a rise in the demand for professionals skilled in ML across industries.
The AI market in India was valued at INR 472.73 Bn in 2020. It is anticipated to reach INR 2113.60 Bn by 2027, expanding at a CAGR of ~24.17% during the 2021 2027 period. The global machine learning market was valued at INR 839.55 Bn in 2020. It is expected to reach INR 7632.45 Bn by 2027, expanding at a CAGR of ~37.16% during the 2021 2027 period. AI adoption has become significant in various corporations, with employees from non-technological backgrounds incorporating AI processes into their functional roles.
Impact of COVID-19:
COVID-19 has impacted businesses, economies, as well as management strategies employed by corporations. Businesses are having difficulty meeting customer expectations regarding process optimization and increased security concerns due to the rise in connectivity issues.
The demand for cloud-based collaboration tools, content management solutions, and online streaming platforms has picked up. All organizations use analytics to improve decision-making and automate processes for increased productivity and cost-effectiveness. To meet the demands of clients, new entrants use machine learning for activities ranging from designing games, translating language, predicting future market trends, composing music, as well as diagnosing diseases.
Key deterrents to the growth of the market:
Customers often show concerns about sharing information since their sensitive data may get leaked resulting in difficulty in implementation of cloud-based ML applications for most entrepreneurs. The infrastructure of the IT industry in third-world countries is not developed enough to enhance cloud-based business activities. When system requirements are omitted or not fully met due to human error intervention in the development, testing, or verification processes, system defects in data flow occur.