Pune, India -- (SBWIRE) -- 06/26/2020 -- The framework for machine learning includes numerous components including hardware, software, and services. The AI and IoT market is currently rising, requiring high computing capabilities for machine learning technology. A number of companies are constructing AI-optimized hardware components such as memory, processors, and network equipment. The machine learning hardware components are composed of processors, memory chips, and network.

The machine learning software includes solutions for various applications and compatible high-computing applications platforms. The programmers may develop the algorithms according to the application and business requirements. The software consists of libraries and a development software kit (SDK) which can be used to re-program. Machine learning provides technical and managed services that help businesses understand and exploit the advantages of machine learning algorithms; this helps companies to concentrate on their business operations. The services were sub-segmented into managed and technical services. Machine learning is a fast-changing technology for enterprises. It helps businesses to identify trends in the data, which is used to gain market competitive advantage. Depending on the size of the organization, the degree of adoption of machine learning technology differs. It is expected that the advent of connected AI will allow ML algorithms to learn continuously, based on newly available knowledge. These technologies are expected to boost the demand in years to come.

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Quality control, root cause analysis, predictive maintenance and supply-chain optimization are some of the key areas which are being focused in the global machine learning in automobile industry. Automotive industry executives believe machine learning can aid in achieving marketing goals, but that doesn't unavoidably mean they are investing in that ambition. Machine learning (ML) is mostly linked with innovations in the automotive industry, viz. parking & lane change assists, intelligent energy systems and self-driving cars. But ML also has a major impact on the marketing process, from how automotive marketers set goals and calculate returns on their investments to how they interact with customers. ML is poised to become as much a guiding principle as an empirical element for sophisticated industry-wide marketing campaigns. This is especially true in the automotive industry, being a high-tech and capital-intensive sector fascinating through disruption. While most players in the automotive sector invest in ML for their marketing campaigns, there are incentives and key performance indicators (KPIs) put in place by a much smaller community to utilize more automation and ML. Closing the gap necessitates a greater commitment to improving not only useful but also beneficial ML capability. From parts suppliers to vehicle manufacturers, service providers to rental car companies, the automotive and related mobility industries are greatly gaining from implementing machine learning on a scale. There are huge opportunities for machine learning throughout the automotive value chain, to improve both processes and products.

The global machine learning in automobile market is segmented on the basis of component, enterprise size and geography. Based on the component, the global market is bifurcated into hardware, software and services. On the basis of enterprise size, the global machine learning in automobile market is segmented into SMEs and large enterprises. Geographically, the market is divided into North America, Europe, Asia-Pacific (APAC), Latin America and Middle East & Africa (MEA).

The key players operating within the global machine learning in automobile market are Baidu Inc., Amazon Web Services, Inc. H2O.ai, Google Inc., International Business Machines Corporation, Intel Corporation, Microsoft Corporation, Hewlett Packard Enterprise Development LP, SAP SE and SAS Institute Inc. among others.

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Major Point of TOC:
Chapter One: Market Introduction
Chapter Two: Executive Summary
Chapter Three: Global Machine Learning in Automobile Market – Key Product Type Dynamics
Chapter Four: Global Machine Learning in Automobile Market – Analysis and Revenue Forecast, 2015-2025 – By Product Type
Chapter Five: Global Machine Learning in Automobile Market – Analysis and Revenue Forecast, 2015-2025 – By Channel Type
Chapter Six: Global Machine Learning in Automobile Market – Analysis and Revenue Forecast, 2015-2025 – By Region
Chapter Seven: North America Machine Learning in Automobile Market – Analysis and Revenue Forecast, 2015-2025
Chapter Eight: Europe Machine Learning in Automobile Market – Analysis and Revenue Forecast, 2015-2025
Chapter Nine: Asia-Pacific Machine Learning in Automobile Market – Analysis and Revenue Forecast, 2015-2025
Chapter Ten: Latin America Machine Learning in Automobile Market – Analysis and Revenue Forecast, 2015-2025
Chapter Eleven: Middle East & Africa Machine Learning in Automobile Market – Analysis and Revenue Forecast, 2015-2025
Chapter Twelve: Competitive Analysis
Chapter Thirteen: Appendix
Chapter Fourteen: Research Conclusions

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