人工智慧軟體

本報告調查了全球人工智慧軟體市場，總結了市場影響因素和市場機會分析、人工智慧軟體收入趨勢和預測，以及按框架和地區等各個細分領域進行的詳細分析。

報告好處：

• 制定人工智慧硬體和軟體策略，同時展望未來的收入機會
• 在廣泛的市場動態中規劃您的投資和創新，並識別 "熱門" 收入機會。
• 了解人工智慧軟體的主要趨勢，包括邊緣人工智慧與雲端人工智慧、開源與閉源、區域發展和消費模式。

關鍵問題的答案：

• 企業採用生成式人工智慧將在多大程度上刺激自然語言處理（NLP）軟體收入？
• 不同人工智慧框架的邊緣人工智慧軟體收入機會有何不同？
• 人工智慧軟體收入中有多少比例來自開源？
• 哪些人工智慧軟體工具可以提供最大的未來市場機會？

研究亮點：

• 按每個軟體工具劃分的軟體收入的詳細分類
• 按地區劃分的軟體收入綜合明細
• 預測邊緣人工智慧與雲端人工智慧的收入機會
• AI軟體消費模型的思考
• 開源和閉源收入預測

目錄

• 表格
• 圖表

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