人工智慧软体

本报告调查了全球人工智慧软体市场，总结了市场影响因素和市场机会分析、人工智慧软体收入趋势和预测，以及按框架和地区等各个细分领域进行的详细分析。

报告好处：

• 制定人工智慧硬体和软体策略，同时展望未来的收入机会
• 在广泛的市场动态中规划您的投资和创新，并识别 "热门" 收入机会。
• 了解人工智慧软体的主要趋势，包括边缘人工智慧与云端人工智慧、开源与闭源、区域发展和消费模式。

关键问题的答案：

• 企业采用生成式人工智慧将在多大程度上刺激自然语言处理（NLP）软体收入？
• 不同人工智慧框架的边缘人工智慧软体收入机会有何不同？
• 人工智慧软体收入中有多少比例来自开源？
• 哪些人工智慧软体工具可以提供最大的未来市场机会？

研究亮点：

• 按每个软体工具划分的软体收入的详细分类
• 按地区划分的软体收入综合明细
• 预测边缘人工智慧与云端人工智慧的收入机会
• AI软体消费模型的思考
• 开源和闭源收入预测

目录

• 表格
• 图表

Actionable Benefits:

• Develop Artificial Intelligence (AI) hardware and software strategies with visibility over future revenue opportunities.
• Plan investment and innovation considering wider market dynamics and identify "hot" revenue opportunities.
• Become cognizant of key trends in AI software, including edge versus cloud AI, open versus closed source, regional developments, and consumption models.

Critical Questions Answered:

• To what extent will generative AI enterprise adoption stimulate Natural Language Processing (NLP) software revenue?
• How will edge AI software revenue opportunities vary across different AI frameworks?
• What share of AI software revenue will result from open-source?
• Which AI software tools offer the biggest market opportunity moving forward?

Research Highlights:

• A detailed breakdown of software revenue by each software tool.
• Comprehensive breakdown of software revenue by region.
• Forecast of edge against cloud AI revenue opportunities.
• Examination of AI software consumption models.
• Forecast of open and closed-source revenue opportunities.

Who Should Read This?

• AI software decision makers looking to refine their technical and commercial strategies.
• AI hardware leaders building product alignment and new capabilities in the software market.
• Market strategists aligning entry, competitor, and growth approaches to key software opportunities.
• Cloud provider innovation leaders looking to build a strong position in the AI software market across different frameworks and regions.
• Enterprise leaders and professional investors assessing the direction of the AI software market.

Table of Contents

Tables

• Table 1: Artificial Intelligence Software Revenue by Framework
• Table 2: Artificial Intelligence Software Revenue by Tool
• Table 3: Artificial Intelligence Software Revenue by Type
• Table 4: Artificial Intelligence Software Revenue by Region
• Table 5: Artificial Intelligence Software Revenue by Deployment Location
• Table 6: Artificial Intelligence MLOps Tool Revenue by Interface Type
• Table 7: Artificial Intelligence Software Revenue by Workload Type
• Table 8: Artificial Intelligence Software Revenue by Tool Type
• Table 9: Artificial Intelligence Software Revenue by Consumption Model
• Table 10: Artificial Intelligence Software Revenue by Source Code
• Table 11: Open-Source Software Revenue by Artificial Intelligence Framework
• Table 12: Closed-Source Software Revenue by Artificial Intelligence Framework
• Table 13: Edge AI Software Revenue by Artificial Intelligence Framework
• Table 14: Cloud AI Software Revenue by Artificial Intelligence Framework
• Table 15: Computer Vision Software Revenue by Region
• Table 16: Computer Vision Software Revenue by Tool
• Table 17: Computer vision Software Revenue by Deployment Location
• Table 18: Computer vision Software Revenue by Consumption Model
• Table 19: Computer vision Software Revenue by Source Code
• Table 20: Natural Language Processing Software Revenue by Region
• Table 21: Natural Language Processing Software Revenue by Tool
• Table 22: Natural Language Processing Software Revenue by Deployment Location
• Table 23: Natural Language Processing Software Revenue by Consumption Model
• Table 24: Natural Language Processing Software Revenue by Source Code
• Table 25: Graph-Based AI Models Software Revenue by Region
• Table 26: Graph-Based AI Models Software Revenue by Tool
• Table 27: Graph-Based AI Models Software Revenue by Deployment Location
• Table 28: Graph-Based AI Models Software Revenue by Consumption Model
• Table 29: Graph-Based AI Models Software Revenue by Source Code
• Table 30: Computer Vision Edge AI Software Revenue by Tool
• Table 31: Computer Vision Cloud AI Software Revenue by Tool
• Table 32: Natural Language Processing Edge AI Software Revenue by Tool
• Table 33: Natural Language Processing Cloud AI Software Revenue by Tool
• Table 34: Graph-Based AI Models Edge AI Software Revenue by Tool
• Table 35: Graph-Based AI Models Cloud AI Software Revenue by Tool
• Table 36: Edge AI Software Revenue by Workload Interface
• Table 37: Cloud AI Software Revenue by Workload Interface
• Table 38: Edge AI Software Revenue by Source Code
• Table 39: Cloud AI Software Revenue by Source Code
• Table 40: Computer Vision Inference Software Revenue
• Table 41: Computer Vision Training Software Revenue
• Table 42: Natural Language Processing Inference Software Revenue
• Table 43: Natural Language Processing Training Software Revenue by Tool
• Table 44: Graph-Based AI Model Inference Software Revenue by Tool
• Table 45: Graph-Based AI Model Training Software Revenue by Tool
• Table 46: Training Software Revenue
• Table 47: Inferencing Software Revenue

Charts

• Chart 1: Artificial Intelligence Software Revenue by Framework
• Chart 2: Artificial Intelligence MLOps Tool Revenue by Type
• Chart 3: Edge AI Software Revenue by Framework
• Chart 4: Artificial Intelligence Software Revenue by Skill Level
• Chart 5: Comparing Regional AI Software Revenue
• Chart 6: Natural Language Processing Software Revenue by Type
• Chart 7: AI Software Revenue by Deployment Location
• Chart 8: Regional AI Software Revenue by Framework
• Chart 9: Cloud and Edge AI Software Revenue by Source Code
• Chart 10: Cloud and Edge AI Software by Workload Interface