Rapid developments in the video analytics domain and increasing adoption of advanced technologies in the security and surveillance industry have resulted in the development of high-performance AI-capable processors such as GPU and TPU, which have higher memory bandwidth and computational capability as compared to traditional processors, i.e., central processing units (CPUs). The evolution of technologies, namely machine learning and artificial intelligence (AI), has generated the demand for cognitive computing technology across various verticals such as automotive, industrial, and consumer. Deep learning is also used for extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks. It is an important technique used for analyzing massive amounts of unsupervised data, making it a valuable tool for big data analytics wherein the raw data is largely unstructured. The deep learning technique is used to extract high-level, complex abstractions from data through a hierarchical learning process. Big data has become important to many public and private organizations wherein massive amounts of domain-specific information is generated, which can contain useful information on national intelligence, cybersecurity, fraud detection, marketing, and medical informatics. Big data analytics is the process of scrutinizing large datasets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other actionable insights. Thus, the increasing adoption of cloud-based technology is necessitating the need for deep learning.īig data analytics has also played a pivotal role in the growth of cloud services. Cloud-based solutions are ideal for small and midsized businesses that find on-premises solutions costlier. Most companies and startups do not develop their own specialized hardware or software to apply deep learning to their specific business needs. A growing number of tech giants and startups have begun offering machine learning as a cloud service due to the burgeoning demand for AI-based computation. Therefore, we can say the COVID-19 will drive the market for certain industries.ĭata center accelerator Market Dynamics Driver: Growth of cloud-based servicesĭeep learning services being made available over the cloud are reducing the initial costs associated with executing business operations and curtailing server maintenance tasks. However, the adoption of AI is expected to grow. Several industries are worse hit by this pandemic, but some industries are benefiting from this pandemic. ![]() ![]() Furthermore, a personalized online tutorial company Squirrel AI uses AI-based adaptive learning to curate lessons for a student. ![]() For instance, Coursera has launched an AI-powered tool called the CourseMatch that helps schools and universities identify courses on the platform that matches their curriculum. Ed-tech firms have deployed AI tools to enhance online learning and virtual classroom experience for students. Currently, AI is being used for predictive maintenance and will further be implemented to forecast demand and returns in the supply chain.ĬOVID-19 has impacted the educational industries rather positively, with ed-tech companies adopting AI technology to impart education during the lockdown. By adopting these technologies, companies can cut costs, increase process efficiency, and reduce human contact significantly. Post COVID-19, the manufacturing sector is expected to scale up smart manufacturing processes using AI, IoT, and blockchain technologies. To know about the assumptions considered for the study, Request for Free Sample Report COVID-19 Impact on the Global Data center accelerator market The global data center accelerator market size is projected to grow from USD 13.7 billion in 2021 to USD 65.3 billion by 2026 it is expected to grow at a CAGR of 36.7% from 2021 to 2026.įactors such as growing demand for deep learning and surge in demand for cloud-based services are driving the growth of the market during the forecast period.
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