The deep learning market in the US to grow at a CAGR of 57.29% during the period 2017-2021.
The report, Deep Learning Market in the US 2017-2021, has been prepared based on an in-depth market analysis with inputs from industry experts. The report covers the market landscape and its growth prospects over the coming years. The report also includes a discussion of the Key vendors operating in this market.
One trend in the market is advances in deep learning. With industries harnessing deep learning technology to optimize operations and make real-time decisions, modular capabilities in deep learning will aid visual design, configuration, and training new models obtained from pre-existent building blocks. A major structural change will emerge as a result, known as transfer learning, which will enable experiential solving of similar cases.
According to the report, one driver in the market is enabling condition monitoring in industries. Deep learning facilitates condition monitoring to map the overall equipment effectiveness (OEE) across plant level to generate real-time data regarding assets deployed on the shop floor. Post implementation of deep learning in industries, end-users can leverage technology as a medium to gain knowledge regarding the productivity and machine condition. It has been observed that the overall equipment effectiveness across industries with deep learning technology increased from 65% to 85% with an average improvement of 17-20% across all industries.
Further, the report states that one challenge in the market is technical difficulties encountered. Deep learning technology, which deploys artificial neural networks to make real-time decisions, requires voluminous data to form a knowledge base in order to interpret the behavior of data set. However, carrying out the data acquisition is an arduous task and is also considered unethical. In addition, most of the companies specializing in deep learning technology and AI are small- to mid-sized start-ups. These companies are rated high on their technical capability but lack sufficient funds to gather massive chunk of data.