Machine Learning is a part of Artificial Intelligence (AI) that grants computers the capability to learn without being detailed programmed. It mainly focuses on the advancement of the computers programs that can be switch when exposed to new data. It helps the computer to find the hidden insights without being explicitly programmed where to look. It has multiple uses in today’s technology market concerning with safety and security such as face detection, face recognition, Image classification, Speech recognition, antivirus , Google, antispam, genetic, signal diagnosing , whether forecast and many more.
The machine learning report can be segmented into submarket such as by components, by enterprise application, by organization size and by region. The components of machine learning can be segmented as software tools, Cloud and web-based Application Programming Interface (APIs) and others such as model validator, decision report/predictor/training, and report storage. The software tools is further subdivided into data storage & archiving and modelling & processing.
The global Machine Learning Market is poised for growth during the forecast period of 2018-2022. Last year i.e. in 2017, the evaluated value of the market was around USD 1.35 million. By the end of the forecast period the market is expected to earn USD 8.54 million. The market is anticipated to grow at a jaw-dropping CAGR of 43.9%.
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The prominent players in the Machine Learning Market are – Google Inc. (U.S.), Facebook (U.S), ,IBM Watson (U.S.), Baidu (China), Apple (U.S), Microsoft (U.S.), Cisco(U.S.), Wipro(India), and Nuance Communications(U.S.) , Amazon (U.S) , Intel (U.S.).
December 2017, Apple commits ‘Turi Create’ machine learning development tool to GitHub. The company claims a new machine learning framework designed to help developers build machine learning models that can be parlayed into apps running on its major operating systems. Turi Create is designed to assist in the development and deployment of machine models, enabling the developers to add recommendations, object detection, image classification, image similarity or activity classification to their app, among other assets. This framework can be used to create a system that can recommend things based on its learning from the surrounding, image classification, image similarity, object detection, text classification and activity classification. All of these capabilities can enable many industries like automotive, media & entertainment and education to classify, implement and conclude things in a better way.
December, 2017, AI, Machine Learning to Be Used by Hackers in 2018: Symantec. Tech giant Symantec claims that cybercriminals will use the artificial intelligence and machine learning to explore weaker, victim networks. Additionally, the internet of things (IoT) devices will be hijacked and used in the distributed denial of service (DDos) attacks. The security tech giant also claimed that 2018 will see artificial intelligence vs. artificial intelligence in a cyber-security context, as in 2017, it had seen massive DDos attacks using hundreds of IoT devices across homes and offices. However, the advancement in machine learning is going to be a major driving factor for the growth of this market in terms of tackling with the security concerns by machines while adopting learning.
December, 2017, NVIDIA Introduces Titan V for Machine Learning Acceleration on the PC. In the past TITAN targeted gamers (TITAN X) or machine learning scientists (TITAN XP). NVIDIA, at the annual NIPS (Neural Information Processing Systems) conference announced the introduction of its new “TITAN V” PC GPU. TITAN V is targeted at machine learning scientists. TITAN V’s 21.1 billion transistors are capable of delivering 110 teraflops of performance. This immense amount of performance makes the TITAN V ideally suited for users looking to explore computational processing for scientific simulation and other deep learning/AI applications on their desktop PCs
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The worldwide machine learning market has been divided into 5 distinct parts that include, applications, components, services, organization sizes & regions.
On the basis of applications, the market is divided into BSFI, automotive, healthcare & government.
On account of components, the market has been segmented into cloud, web-based APIs & software tools.
The services segment can be further bifurcated into professional services & managed services. Out of the two managed services section is estimated to grow tremendously over the forecast period. It is because managed service suppliers can look after all the hardware & software functions. With them all the organizations can be at peace as they only need to install & update applications.
The organizational sizes portion has been split into large enterprises & small & medium enterprises.
Geographically, the global market for machine learning is spread across North America, Europe, Asia Pacific & Rest of the World.
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