Global AI Infrastructure Market report from Global Insight Services is the single authoritative source of intelligence on AI Infrastructure Market. The report will provide you with analysis of impact of latest market disruptions such as Russia-Ukraine war and Covid-19 on the market. Report provides qualitative analysis of the market using various frameworks such as Porters’ and PESTLE analysis. Report includes in-depth segmentation and market size data by categories, product types, applications, and geographies. Report also includes comprehensive analysis of key issues, trends and drivers, restraints and challenges, competitive landscape, as well as recent events such as M&A activities in the market.
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AI infrastructure refers to the hardware and software components that are necessary to support artificial intelligence applications and services. This includes things like servers, storage, networking, and software tools. AI infrastructure must be able to handle the large amounts of data that are generated by AI applications and provide the computing power necessary to run complex algorithms.
Key Trends
There are four key trends in AI infrastructure technology:
- Increased demand for data storage and processing power: As AI applications become more sophisticated, they require more data to be stored and processed. This has led to an increase in demand for data storage and processing power, both in the cloud and on-premises.
- The need for more specialized hardware: AI applications are often computationally intensive, requiring specialized hardware such as GPUs and TPUs to run efficiently. This trend is likely to continue as AI applications become more complex.
- The rise of edge computing: Edge computing is becoming increasingly important for AI applications that need to run in real-time or near-real-time.
- The importance of open source software: Open source software is increasingly being used for AI applications, due to the flexibility and customizability it offers.
Key Drivers
There are a few key drivers of the AI infrastructure market.
Firstly, the increasing demand for AI-enabled services is driving companies to invest in AI infrastructure. This is because AI infrastructure is required in order to support the development and deployment of AI-enabled services.
Secondly, the increasing availability of AI infrastructure is making it more affordable for companies to invest in. This is because AI infrastructure is becoming more available as more companies invest in it and as more companies offer it as a service.
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Finally, the increasing need for data is driving companies to invest in AI infrastructure. This is because AI infrastructure is required in order to store and process the large amounts of data required for AI applications.
Restraints & Challenges
The key restraints and challenges in the AI Infrastructure market include the need for high investment, the lack of a skilled workforce, and the concerns over data privacy and security.
High investment is needed in order to develop and implement AI infrastructure.
The lack of a skilled workforce is another challenge as there is a lack of people with the necessary skills to develop and operate AI systems.
There are also concerns over data privacy and security, as AI systems often require access to large amounts of data.
Market Segments
The AI infrastructure market is segmented by technology, function, end-user, and region. By technology, the market is classified into machine learning, and deep learning. On the basis of function, it is bifurcated into training, and interference. Based on end-user, it is divided into enterprises, government, and cloud service providers. Region-wise, the market is segmented into North America, Europe, Asia Pacific, and Rest of the World.
Key Players
The global AI infrastructure market includes players such as Amazon Web Services, Cisco Systems Inc, Dell, Google LLC, Intel Corporation, International Business Machines Corporation, Micron Technology Inc, Microsoft Corporation, Nvidia Corporation, Oracle Corporation, and others.
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