Artificial Neural Network Market Growing at a CAGR 20.5% | Key Player Google, IBM, Microsoft, Oracle, Intel

Artificial Neural Network Market Growing at a CAGR 20.5% | Key Player Google, IBM, Microsoft, Oracle, Intel

“Google (US), IBM (US), Oracle (US), Microsoft (US), Intel (US), Qualcomm (US), Alyuda (US), Ward Systems (US), GMDH, LLC (US), Starmind (Switzerland), NeuralWare (US), Neurala (US), and Clarifai (US).”
Artificial Neural Network Market by Component (Solutions, Platform/API and Services), Application (Image Recognition, Signal Recognition, and Data Mining), Deployment Mode, Organization Size, Industry Vertical, and Region – Global Forecast to 2024

The global Artificial Neural Network (ANN) Market size is expected to grow from USD 117 million in 2019 to USD 296 million by 2024, at a Compound Annual Growth Rate (CAGR) of 20.5% during the forecast period. The major driving factor in the ANN market is due to the growing demand to train a large volume of data sets with low supervision to drive the market

The government and defense segment to hold a larger market size during the forecast period

The ANN market is segmented on the basis of applications. The applications include image recognition, signal recognition, data mining, and others (recommender system and drug discovery). The data mining segment is expected to grow at a rapid pace in the coming years in the ANN market. It is one of the leading applications in the ANN market due to growing demand to extract hidden predictive information from huge databases.

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Among services, the consulting services segment to grow at a higher CAGR during the forecast period

The ANN market is segmented based on services into two categories, namely, managed services and professional services. The professional services is further bifurcated into consulting services, support and maintenance services and deployment and integration. The consulting services segment is expected to grow at a rapid pace during the forecast period, as consulting services help organizations to utilize ANN tools capabilities that uses graph structures for semantic queries with nodes, edges, and properties to represent and store connected data. The vendors in this market offer services to industrial customers or end-users so that they can effectively manage and deploy ANN solutions.

The major ANN vendors include Google (US), IBM (US), Oracle (US), Microsoft (US), Intel (US), Qualcomm (US), Alyuda (US), Ward Systems (US), GMDH, LLC (US), Starmind (Switzerland), NeuralWare (US), Neurala (US), and Clarifai (US). These players have adopted various growth strategies, such as partnerships, agreements, and collaborations; and new product launches, to further expand their presence in the global ANN market. Partnerships and new product launches have been the most dominating strategy adopted by the major players from 2017 to 2019, which has helped them to innovate on their offerings and broaden their customer base.

Google (US) is one of the global technology leaders, and its primary areas include advertising, search, operating systems and platforms, and enterprise and hardware products. Its search segment offers a vast index of websites and other online contents that are made available through its search engine. The company has an integrated research organization called Google Brain Team for conducting DNN and machine learning research. Google has been making certain developments in the field of the neural network to augment its product offerings and bring about higher technological innovations. In November 2015, Google introduced TensorFlow, an open-source AI engine that deploys deep-learning technology for its computational operations.

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IBM (US) was founded in 1911 and is headquartered in New York, US. It majorly operates in 5 business segments, namely, cognitive solutions, global business services, technology services and cloud platforms, systems, and global financing. The company has developed IBM Watson, a business unit for cognitive computing technology. This AI-based technology falls under the purview of IBM’s cognitive computing. Its experiment-centric deep- learning service within IBM Watson Studio enables data scientists to design their neural networks visually and scale-out their training runs, along with auto-allocation, which means paying only for the resources used. The company’s innovative and cost-effective products offer it a competitive advantage over others.

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