Automotive, healthcare, retail, manufacturing, and banking, financial services, and insurance (BFSI) companies around the world are increasingly adopting cloud computing platforms, as they offer hosted data storage solutions, which help organizations in substantial cost savings. Cloud computing serves as an ideal platform for deep learning algorithms, as the architecture provides support for scalability and virtualization and offers storage for a large volume of structured and unstructured data.
Deep learning-enabled cloud platforms define the ability of a network, processor, or system to manage a huge volume of data efficiently. Thus, the increasing adoption of cloud computing platforms in end-use industries will help the deep learning market advance at an exceptional CAGR of 35.2% during 2020–2030. According to P&S Intelligence, the market revenue stood at $3.7 billion in 2019 and it will reach $102.4 billion by 2030.
Additionally, the mounting investments being made by public and private organizations in information technology (IT) infrastructure will also result in the widespread adoption of deep learning solutions in the coming years, owing to their ability to analyze a large volume of data. Deep learning technology is used in signal recognition, data mining, image recognition, natural language processing (NLP), and recommendation engine applications.
In the coming years, deep learning solutions will be widely used in NLP applications, due to the surging need to integrate deep learning and NLP in chatbots and voice assistants to increase machine–human interactions, as these technologies help chatbots and voice assistants understand customer queries and respond accordingly, without manual intervention.
Thus, the surging shift of end-use industries toward cloud computing and the increasing IT expenditure will facilitate the adoption of deep learning solutions, globally.
Deep learning-enabled cloud platforms define the ability of a network, processor, or system to manage a huge volume of data efficiently. Thus, the increasing adoption of cloud computing platforms in end-use industries will help the deep learning market advance at an exceptional CAGR of 35.2% during 2020–2030. According to P&S Intelligence, the market revenue stood at $3.7 billion in 2019 and it will reach $102.4 billion by 2030.
Additionally, the mounting investments being made by public and private organizations in information technology (IT) infrastructure will also result in the widespread adoption of deep learning solutions in the coming years, owing to their ability to analyze a large volume of data. Deep learning technology is used in signal recognition, data mining, image recognition, natural language processing (NLP), and recommendation engine applications.
In the coming years, deep learning solutions will be widely used in NLP applications, due to the surging need to integrate deep learning and NLP in chatbots and voice assistants to increase machine–human interactions, as these technologies help chatbots and voice assistants understand customer queries and respond accordingly, without manual intervention.
Thus, the surging shift of end-use industries toward cloud computing and the increasing IT expenditure will facilitate the adoption of deep learning solutions, globally.
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