Report Ocean has released a research study titled “AI Infrastructure Market 2023 Forecast to 2031 Analysis by Market Trends.” This study offers precise economic forecasts, global assessments, and country-level insights. It provides a comprehensive view of the competitive market and includes an in-depth supply chain analysis to identify significant industry changes. The study explores the current market status, anticipated future growth, technological advancements, investment opportunities, market economics, and financial data. It thoroughly examines the market and offers insights based on a SWOT analysis of the industry.
AI Infrastructure Market is valued approximately USD 38.34 billion in 2021 and is anticipated to grow with a healthy growth rate of more than 20.59 % over the forecast period 2022-2028. Artificial intelligence(AI) infrastructure involves use of deep learning machine technology upon which organizations can built intelligent self-predictive applications that requires minimal human assistance . Increasing adoption of cloud machine learning platform is key driving factor for the growth of global AI infrastructure market. organizations are beginning to recognize value of incorporation of AI into business as it reduces cost and increases proficiency through automation of process flow.
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In February 2019, IBM launched a new set of IoT solutions. The solutions team up advanced analytics and AI to aid asset-intensive companies to improve their maintenance strategies. The IoT solutions are designed with an aim to help companies to cut down the costs and risk of failure from assets such as turbines, vehicles, manufacturing robots, and mining equipment among others. Also, expanding utilization of AI Intelligence in industries like healthcare, automotive, BFSI, and tourism is expected to bolster the market growth of AI infrastructure. However, lack of AI hardware experts in the field is restraining the growth of AI infrastructure market.
Major market player included in this report are:
Cisco (US)
IBM (US)
Intel Corporation (US)
SAMSUNG (South Korea)
Google (US)
Microsoft (US)
Micron Technology, Inc (US)
NVIDIA Corporation (US)
Oracle (US)
Arm Limited (UK)
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Certainly, here are eight key trends in the AI Infrastructure market:
- AI Hardware Acceleration: The use of specialized hardware accelerators, such as GPUs, TPUs, and FPGAs, to boost AI model training and inference performance.
- Edge AI Computing: The deployment of AI infrastructure at the edge to enable real-time processing, reducing latency and supporting applications like autonomous vehicles and IoT devices.
- AI-Optimized Cloud Services: Cloud providers offering AI-optimized infrastructure services, such as AI chips and GPU instances, to support machine learning and deep learning workloads.
- AI Software Stack: The development of AI software stacks and frameworks for model development and deployment, simplifying the AI development process.
- AI Co-Processors: The rise of AI co-processors and AI-specific hardware components in data centers to handle AI workloads efficiently.
- Green AI: A focus on energy-efficient AI infrastructure to reduce power consumption and minimize the environmental impact of AI model training.
- AI Operations (AIOps): The integration of AI infrastructure for IT operations and monitoring to enhance the performance and security of IT systems.
- AI Infrastructure as a Service (AIaaS): The emergence of AI infrastructure as a service, offering on-demand access to AI hardware and software resources, reducing the barrier to entry for AI development.
The detailed segments and sub-segment of the market are explained below:
By Offering:
Hardware
Software
By Deployment
On-Premises
Cloud
Hybrid
By Technology:
Machine Learning
Deep Learning
By End-User :
Enterprises
Government Organizations
Cloud Service Provider
By Function:
Training
Inference
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By Region:
North America
U.S.
Canada
Europe
UK
Germany
France
Spain
Italy
ROE
Asia Pacific
China
India
Japan
Australia
South Korea
RoAPAC
Latin America
Brazil
Mexico
Rest of the World
Table of Contents:
- Executive Summary
- Introduction
- Methodology
- Market Overview
- Market Analysis
- Market Size and Forecast
- Market Trends
- Market Dynamics
- Market Segmentation
- Competitive Landscape
- Competitive Scenario
- Key Players
- Regional Analysis
- Regional Market Overview
- Market Size and Forecast by Region
- Product/Service Analysis
- Product/Service Overview
- Market Size and Forecast by Product/Service
- End-User Analysis
- End-User Overview
- Market Size and Forecast by End-User
- Industry Structure
- Key Findings and Recommendations
- Conclusion
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