Deep Learning Market size is expected to show notable proceeds between the period of 2021 to 2027 on account of the increasing adoption of cloud-based solutions and the depleting hardware costs. The increasing amount of generated data across several end-use industries has propelled the need for enhanced computing power. Furthermore, the mounting number of investments for the development of machines and deep learning will further influence the market growth.
The increasing acceptance of automated predictive analytics and the higher integration of deep learning with big data analytics are other promising factors adding to the industry expansion.
Based on offering, the deep learning market is classified across hardware, software, and services. Out of these, the software segment is expected to witness substantial growth owing to the higher adoption of smartphone assistants. Deep learning-based software that is optimized for factory automation allows companies to build ground-breaking inspection systems with industrial automation. These systems offer effective human visual inspection with reliability, repeatability, as well as the power of computerized systems. The increasing requirement for software that pops up ads on multiple websites is another promising factor fostering the industry expansion.
In terms of application, the image recognition segment is anticipated to be a prominent revenue source for the deep learning industry driven by the growing penetration in the gaming sector and the rising influx of self-driving cars. The higher need for facial & pattern recognition along with digital image processing will drive the segmental growth.
The demand for deep learning in data mining will expand commendably over 2021-2027 with the rising adoption in the AI sector. Increasing demand for machine translation and bioinformatics will additionally bolster the deep learning market outlook.
With respect to end-use, the deep learning industry size from the security segment will grow at a considerable pace on account of the surge in the number of cyber-attacks.
Regionally, North America deep learning market is poised to attain major traction due to the higher incorporation in consumer-centric solutions across enterprises. The growing need for customer behavior and operations has also amplified the deployment of big data. The advancing IT, healthcare, and defense sectors coupled with the paradigm shift from traditional programming to modern modeling and simulation methods will substantially contribute towards the market growth.
The deep learning market consolidates the presence of major participants, including Qualcomm Technologies, Inc. (Qualcomm), NVIDIA Corporation, Advanced Micro Devices, Inc., Hewlett-Packard, Arm Ltd. (Nvidia, SoftBank Group), IBM Corporation, General Vision, Inc., Microsoft Corporation, Google, Inc. (Alphabet Inc.), Intel Corporation, Sensory, Inc., Enlitic, and Clairifai, Inc.
Inorganic strategic measures, such as acquisitions, partnerships, and investments, along with continuous technological advancements are actively employed by these firms to maintain their geographical foothold while sustaining competition.
For instance, Skymind, in March 2019, secured $11.5 million in a Series A funding round in an attempt to bring deep learning to multiple enterprises as well as to build out its team in North America and accelerate its regional customer acquisition.
Likewise, Baidu, Inc, in September 2020, disclosed a raft of new features within its Baidu Brain AI technology platform, which saw an update to version 6.0. The platform’s foundation layer is based on the company’s deep learning platform PaddlePaddle.
While the global economic turbulence crafted by the ongoing COVID-19 outbreak paved the path for supply chain disruptions, it also posed roadblocks for several industrial and commercial verticals across the world. However, the pandemic acted as a booster for the deep learning industry for the early detection as well as the diagnosis of the infection. Medical and computer researchers are increasingly adopting machine-learning models in order to analyze the radiology images of infected patients.