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市場調查報告書

巨量資料及IoT環境的人工智能 (AI) 、機器學習 (ML):資料擷取、分析、決策的市場:2016-2021年

Artificial Intelligence and Machine Learning in Big Data and IoT: The Market for Data Capture, Analytics, and Decision Making 2016 - 2021

出版商 Mind Commerce 商品編碼 363176
出版日期 內容資訊 英文 190 Pages
商品交期: 最快1-2個工作天內
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巨量資料及IoT環境的人工智能 (AI) 、機器學習 (ML):資料擷取、分析、決策的市場:2016-2021年 Artificial Intelligence and Machine Learning in Big Data and IoT: The Market for Data Capture, Analytics, and Decision Making 2016 - 2021
出版日期: 2016年07月13日 內容資訊: 英文 190 Pages
簡介

本報告提供各種人工智能 (AI) 技術與其用途的相關調查,人工智能 (AI) 及機器學習 (ML)的各種技術、用途、方法,巨量資料及IoT環境的AI、ML趨勢,各種區分、各地區/主要國家的市場規模的變化與預測,主要企業、解決方案簡介,相關經營者的建議等彙整資料。

第1章 簡介

第2章 人工智能 (AI)的技術與市場

  • AI和機器學習 (ML)
  • AI的各種類型
  • 企業的AI、ML的利用
  • AI技術
    • 機器學習
    • 自然地語言處理
    • 影像處理
    • 語音辨識
    • 人工神經網
    • 深層學習
    • 其他
  • AI、ML技術的目標
    • 理論發展
    • 知識表現
    • 規劃
    • 通訊
    • 機器知覺
    • 運動操控
    • 社群情報
    • 建立性
    • AGI (通用人工智能)
    • 電腦視覺
    • 機器人工學
  • AI方法
    • 控制論、腦模擬
    • 象徵方法
    • 輔助象徵方法
    • 統計的方法
    • 整合型方法
  • AI工具
    • 搜尋、最佳化
    • Logic
    • 概率
    • 分類孩子、統計
    • 神經網
    • 深層前饋神經網
    • 深層重歸型神經網
    • 控制理論
    • 語言
  • AI結果
  • 神經網和AI
  • 深層學習和AI
  • 預測分析與AI
  • IoT和巨量資料分析
  • IoT和AI
  • 消費者取向IoT、巨量資料分析、AI
  • 工業IoT、巨量資料分析、ML
  • AI和認知式運算
  • 超人類主義和AI

第3章 巨量資料、IoT的AI、ML

  • ML Everywhere
  • ML API和巨量資料的發展
  • 超規模分析和AI
  • 演算法商務的崛起
  • 雲端託管機器情報
  • ML的矛盾
  • 價值鏈分析
    • AI、ML企業
    • IoT企業
    • 巨量資料分析供應商
    • 連接性解決方案&基礎設施供應商
    • 硬體設備&設備製造商
    • 開發者、資料科學家
    • 終端用戶

第4章 AI和ML的用途、服務

  • 情報性能監控
  • 基礎設施監測
  • 精密模式的生成
  • 建議引擎
  • 塊環鏈、加密技術
  • 企業應用
  • 情境察覺
  • 顧客回饋
  • 自動駕駛車
  • 詐騙偵測系統
  • 個人化醫療、醫療服務
  • 預測資料建模
  • 智慧機器
  • 網路安全解決方案
  • 自規則的代理商
  • 智慧型助手
  • 智慧型決策支援系統
  • 風險管理
  • 資料探勘&管理
  • 智慧型機器人工學
  • 金融技術
  • 機器情報

第5章 AI的預測分析:市場預測、展望

  • 全球市場的預測
    • 全球市場收益的預測
      • 各類型
      • 各商業應用
      • 各核心技術
      • 各技術應用領域
      • 各市場區隔
      • 各產業區隔
    • 終端用戶產業上生產率的預測
    • 系統&硬體設備市場預測
    • 認知式運算市場預測
    • 商務內容的創造
    • 經濟交易
    • 機器人老闆和員工的監督
    • 大樓安全系統
    • 商務分析軟體
    • 智慧機器與僱用
    • 自助服務視覺發現&資料簡報工具
  • 地區市場預測
    • 收益預測:各地區
    • 北美
    • 歐洲
    • 亞太地區
    • 中東、非洲
    • 南美

第6章 AI支援連網型設備的引進預測

  • 全球引進預測
    • 各設備、平台
    • 各引進部門
    • 各核心技術
    • 各引進技術
    • 各市場區隔
    • 各產業區隔
  • 地區的引進預測
    • 北美
    • 歐洲
    • 亞太地區
    • 中東、非洲
    • 南美

第7章 企業分析

  • AI倡議、收購策略
    • Google
    • Twitter
    • Microsoft
    • IBM
    • Apple
    • Facebook
    • Amazon
    • Skype
    • Salesforce
    • Intel
    • Yahoo
    • AOL
    • NVIDIA
    • x.ai
    • Tesla
    • Baidu
    • H2O.ai
    • SparkCognition
    • OpenAI
    • Inbenta
  • 巨量資料、分析、IoT相關企業及解決方案
    • Tachyus
    • Sentrian
    • Maana
    • Veros Systems
    • Neura
    • Augury Systems
    • Glassbeam
    • Comfy
    • mnubo
    • C-B4
    • PointGrab
    • Tellmeplus
    • Moov
    • Sentenai
    • Imagimob
    • FocusMotion
    • MoBagel

第8章 總論、建議

  • 對資料分析供應商的建議
  • 對AI、ML經營者的建議
  • 對IoT經營者、設備製造商的建議
  • 對服務供應商的建議
  • 對企業的建議
目錄

More than 50% of enterprise IT organizations are experimenting with Artificial Intelligence (AI) in various forms such as Machine Learning, Deep Learning, Computer Vision, Image Recognition, Voice Recognition, Artificial Neural Networks, and more. AI is not a single technology but a convergence of various technologies, statistical models, algorithms, and approaches. Machine Learning is a sub-field of computer science that evolved from the study of pattern recognition and computational learning theory in AI.

Every large corporation collects and maintains a huge amount of human-oriented data associated with its customers including their preferences, purchases, habits, and other personal information. As the Internet of Things (IoT) progresses, there will an increasingly large amount of unstructured machine data. The growing amount of human-oriented and machine generated data will drive substantial opportunities for AI and Machine Learning support for unstructured data analytics solutions.

This research evaluates various AI technologies and their use relative to analytics solutions within the rapidly growing enterprise data arena. The report assesses emerging business models, leading companies, and solutions. The report also provides forecasting for unit growth and revenue from 2016 - 2021 associated with AI supported predictive analytics solutions. All purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

Report Benefits:

  • Forecasts for AI in predictive analytics 2016 to 2021
  • Identify highest potential AI technology area opportunities
  • Understand AI strategies and initiatives of leading companies
  • Learn the optimal use of AI for smart predictive analytics in IoT data
  • Understand the AI in Big Data, Analytics, and IoT ecosystem and value chain
  • Identify opportunities for AI in Analytics for IoT and other unstructured data

Key Findings:

  • North America will lead the AI in Big Data and IoT through 2021
  • The AI powered predictive analytics market will reach $18.5 billion by 2021
  • Autonomous robots and intelligent agents will be the top application areas
  • AI will find its way into edge data devices for security and real-time data analytics

Target Audience:

  • Internet of Things companies
  • Artificial Intelligence companies
  • Big Data and analytics companies
  • Robotics and automation companies
  • Cloud and Internet of Things companies
  • Investment firms focused on automation
  • Product and service providers of all types
  • Governments and NGO R&D organizations

Companies in Report:

  • Amazon
  • AOL
  • Apple
  • Augury Systems
  • Baidu
  • C-B4
  • Comfy
  • Facebook
  • FocusMotion
  • Glassbeam
  • Google
  • H2O.ai
  • IBM
  • Imagimob
  • Inbenta
  • Intel
  • Maana
  • Microsoft
  • mnubo
  • MoBagel
  • Moov
  • Neura
  • NVIDIA
  • OpenAI
  • PointGrab
  • Salesforce
  • Sentenai
  • Sentrian
  • Skype
  • SparkCognition
  • Tachyus
  • Tellmeplus
  • Tesla
  • Twitter
  • Veros Systems
  • x.ai
  • Yahoo

Table of Contents

1 Introduction

  • 1.1 Executive Summary
  • 1.2 Research Objectives
  • 1.3 Key Findings
  • 1.4 Target Audience
  • 1.5 Companies in Report

2 Artificial Intelligence Technology and Market

  • 2.1 Artificial Intelligence and Machine Learning
  • 2.2 AI Types
  • 2.3 Use of AI and ML in Enterprise
  • 2.4 Artificial Intelligence Technology
    • 2.4.1 Machine Learning
    • 2.4.2 Natural Language Processing
    • 2.4.3 Image Processing
    • 2.4.4 Voice Recognition
    • 2.4.5 Artificial Neural Network
    • 2.4.6 Deep Learning
    • 2.4.7 Others
  • 2.5 AI and ML Technology Goals
    • 2.5.1 Reasoning
    • 2.5.2 Knowledge Representation
    • 2.5.3 Planning
    • 2.5.4 Learning
    • 2.5.5 Communication
    • 2.5.6 Machine Perception
    • 2.5.7 Motion Manipulation
    • 2.5.8 Social Intelligence
    • 2.5.9 Creativity
    • 2.5.10 Artificial General Intelligence
    • 2.5.11 Computer Vision
    • 2.5.12 Robotics
  • 2.6 AI Approaches
    • 2.6.1 Cybernetics and Brian Simulation
    • 2.6.2 Symbolic
    • 2.6.3 Sub-Symbolic
    • 2.6.4 Statistical
    • 2.6.5 Integration
  • 2.7 AI Tools
    • 2.7.1 Search and Optimization
    • 2.7.2 Logic
    • 2.7.3 Probability
    • 2.7.4 Classifier and Statistics
    • 2.7.5 Neural Network
    • 2.7.6 Deep Feedforward Neural Network
    • 2.7.7 Deep Recurrent Neural Network
    • 2.7.8 Control Theory
    • 2.7.9 Language
  • 2.8 AI Outcomes
  • 2.9 Neural Networks and Artificial Intelligence
  • 2.10 Deep Learning and Artificial Intelligence
  • 2.11 Predictive Analytics and Artificial Intelligence
  • 2.12 Internet of Things and Big Data Analytics
  • 2.13 IoT and Artificial Intelligence
  • 2.14 Consumer IoT, Big Data Analytics, and Artificial Intelligence
  • 2.15 Industrial IoT, Big Data Analytics, and Machine Learning
  • 2.16 Artificial Intelligence and Cognitive Computing
  • 2.17 Transhumanism and Artificial Intelligence

3 Artificial Intelligence and Machine Learning in Big Data and IoT

  • 3.1 Machine Learning Everywhere
    • 3.1.1 Machine Learning as Open Source Technology
    • 3.1.2 Machine Learning and Intelligent Discovery in IoT
    • 3.1.3 Supervised and Unsupervised Machine Learning
    • 3.1.4 Machine Learning as Big Data Analysis Technique
    • 3.1.5 Machine Learning AI Robots
    • 3.1.6 Machine Learning and Data Democratization
  • 3.2 Machine Learning APIs and Big Data Development
    • 3.2.1 Phases of Machine Learning APIs
    • 3.2.2 Machine Learning API Challenges
    • 3.2.3 Top Machine Learning APIs
      • 3.2.3.1 IBM Watson API
      • 3.2.3.2 Microsoft Azure Machine Learning API
      • 3.2.3.3 Google Prediction API
      • 3.2.3.4 Amazon Machine Learning API
      • 3.2.3.5 BigML
      • 3.2.3.6 AT&T Speech API
      • 3.2.3.7 Wit.ai
      • 3.2.3.8 AlchemyAPI
      • 3.2.3.9 Diffbot
      • 3.2.3.10 PredictionIO
    • 3.2.4 Machine Learning API in General Application Environment
  • 3.3 Ultra-Scale Analytics and Artificial Intelligence
  • 3.4 Rise of Algorithmic Business
  • 3.5 Cloud Hosted Machine Intelligence
  • 3.6 Contradiction of Machine Learning
  • 3.7 Value Chain Analysis
    • 3.7.1 AI and Machine Learning Companies
    • 3.7.2 IoT Companies
    • 3.7.3 Big Data Analytics Providers
    • 3.7.4 Connectivity Solution and Infrastructure Providers
    • 3.7.5 Hardware and Equipment Manufacturers
    • 3.7.6 Developers and Data Scientists
    • 3.7.7 End Users

4 Artificial Intelligence and Machine Leaning Applications and Services

  • 4.1 Intelligence Performance Monitoring
  • 4.2 Infrastructure Monitoring
  • 4.3 Generating Accurate Models
  • 4.4 Recommendation Engine
  • 4.5 Block Chain and Crypto Technologies
  • 4.6 Enterprise Applications
  • 4.7 Contextual Awareness
  • 4.8 Customer Feedback
  • 4.9 Self-Driving Cars
  • 4.10 Fraud Detection Systems
  • 4.11 Personalized Medicine and Healthcare Service
  • 4.12 Predictive Data Modelling
  • 4.13 Smart Machines
  • 4.14 Cybersecurity Solutions
  • 4.15 Autonomous Agents
  • 4.16 Intelligent Assistant
  • 4.17 Intelligent Decision Support Systems
  • 4.18 Risk Management
  • 4.19 Data Mining and Management
  • 4.20 Intelligent Robotics
  • 4.21 Financial Technology
  • 4.22 Machine Intelligence

5 AI Powered Predictive Analytics Market Outlook and Forecasts

  • 5.1 Global Market Forecast
    • 5.1.1 Global Market Revenue 2016 - 2021
      • 5.1.1.1 Market by Type 2016 - 2021
      • 5.1.1.2 Market by Business Application 2016 - 2021
      • 5.1.1.3 Market by Core Technology 2016 - 2021
      • 5.1.1.4 Market by Technology Application 2016 - 2021
      • 5.1.1.5 Market by Segment 2016 - 2021
      • 5.1.1.6 Market by Industry Vertical 2016 - 2021
    • 5.1.2 Productivity in Industry Vertical 2016 - 2021
    • 5.1.3 System and Hardware Market 2016 - 2021
    • 5.1.4 Cognitive Computing Market 2016 - 2021
    • 5.1.5 Business Content Creation
    • 5.1.6 Economic Transactions
    • 5.1.7 Robo-Boss and Worker Supervision
    • 5.1.8 Building Security System
    • 5.1.9 Business Analytics Software
    • 5.1.10 Smart Machine and Employment
    • 5.1.11 Self-Service Visual Discovery and Data Penetration Tools
  • 5.2 Regional Market Forecasts
    • 5.2.1 Revenue by Region 2016 - 2021
    • 5.2.2 North America Market Forecasts 2016 - 2021
      • 5.2.2.1 Market by Types
      • 5.2.2.2 Market by Business Application
      • 5.2.2.3 Market by Core Technology
      • 5.2.2.4 Market by Technology Application
      • 5.2.2.5 Market by Segment
      • 5.2.2.6 Market by Industry Verticals
    • 5.2.3 Europe Market Forecasts 2016 - 2021
      • 5.2.3.1 Market by Types
      • 5.2.3.2 Market by Business Application
      • 5.2.3.3 Market by Core Technology
      • 5.2.3.4 Market by Technology Application
      • 5.2.3.5 Market by Segments
      • 5.2.3.6 Market by Industry Verticals
    • 5.2.4 APAC Market Forecasts 2016 - 2021
      • 5.2.4.1 Market by Type
      • 5.2.4.2 Market by Business Application
      • 5.2.4.3 Market by Core Technology
      • 5.2.4.4 Market by Technology Application
      • 5.2.4.5 Market by Segments
      • 5.2.4.6 Market by Industry Vertical
    • 5.2.5 ME&A Market Forecasts 2016 - 2021
      • 5.2.5.1 Market by Type
      • 5.2.5.2 Market by Business Application
      • 5.2.5.3 Market by Core Technology
      • 5.2.5.4 Market by Technology Application
      • 5.2.5.5 Market by Segment
      • 5.2.5.6 Market by Industry Vertical
    • 5.2.6 Latin America Market Forecasts 2016 - 2021
      • 5.2.6.1 Market by Type
      • 5.2.6.2 Market by Business Application
      • 5.2.6.3 Market by Core Technology
      • 5.2.6.4 Market by Technology Application
      • 5.2.6.5 Market by Segment
      • 5.2.6.6 Market by Industry Vertical

6 AI Supported Connected Device Deployment Forecasts

  • 6.1 Global Deployment Forecast 2016 - 2021
    • 6.1.1 Deployment by Device and Platform
    • 6.1.2 Deployment by Application Sector
    • 6.1.3 Deployment by Core Technology
    • 6.1.4 Deployment by Technology Applications
    • 6.1.5 Deployment by Segments
    • 6.1.6 Deployment by Industry Verticals
  • 6.2 Regional Deployment Forecasts 2016 - 2021
    • 6.2.1 Deployment by Region
    • 6.2.2 North America Deployment Forecasts 2016 - 2021
      • 6.2.2.1 Deployment by Device and Platform
      • 6.2.2.2 Deployment by Application Sector
      • 6.2.2.3 Deployment by Core Technology
      • 6.2.2.4 Deployment by Technology Application
      • 6.2.2.5 Deployment by Segment
      • 6.2.2.6 Deployment by Industry Verticals
    • 6.2.3 Europe Deployment Forecasts 2016 - 2021
      • 6.2.3.1 Deployment by Device and Platform
      • 6.2.3.2 Deployment by Application Sector
      • 6.2.3.3 Deployment by Core Technology
      • 6.2.3.4 Deployment by Technology Applications
      • 6.2.3.5 Deployment by Segment
      • 6.2.3.6 Deployment by Industry Vertical
    • 6.2.4 APAC Deployment Forecasts 2016 - 2021
      • 6.2.4.1 Deployment by Device and Platform
      • 6.2.4.2 Deployment by Application Sector
      • 6.2.4.3 Deployment by Core Technology
      • 6.2.4.4 Deployment by Technology Application
      • 6.2.4.5 Deployment by Segment
      • 6.2.4.6 Deployment by Industry Vertical
    • 6.2.5 ME&A Deployment Forecasts 2016 - 2021
      • 6.2.5.1 Deployment by Device and Platform
      • 6.2.5.2 Deployment by Application Sector
      • 6.2.5.3 Deployment by Core Technology
      • 6.2.5.4 Deployment by Technology Application
      • 6.2.5.5 Deployment by Segment
      • 6.2.5.6 Deployment by Industry Vertical
    • 6.2.6 Latin America Deployment Forecasts 2016 - 2021
      • 6.2.6.1 Deployment by Device and Platform
      • 6.2.6.2 Deployment by Application Sector
      • 6.2.6.3 Deployment by Core Technology
      • 6.2.6.4 Deployment by Technology Application
      • 6.2.6.5 Deployment by Segment
      • 6.2.6.6 Deployment by Industry Vertical

7 Company Analysis

  • 7.1 AI Initiatives and Acquisition Strategies
    • 7.1.1 Google
    • 7.1.2 Twitter
    • 7.1.3 Microsoft
    • 7.1.4 IBM
    • 7.1.5 Apple
    • 7.1.6 Facebook
    • 7.1.7 Amazon
    • 7.1.8 Skype
    • 7.1.9 Salesforce
    • 7.1.10 Intel
    • 7.1.11 Yahoo
    • 7.1.12 AOL
    • 7.1.13 NVIDIA
    • 7.1.14 x.ai
    • 7.1.15 Tesla
    • 7.1.16 Baidu
    • 7.1.17 H2O.ai
    • 7.1.18 SparkCognition
    • 7.1.19 OpenAI
    • 7.1.20 Inbenta
  • 7.2 Big Data, Analytics, and IoT Companies and Solutions
    • 7.2.1 Tachyus
    • 7.2.2 Sentrian
    • 7.2.3 Maana
    • 7.2.4 Veros Systems
    • 7.2.5 Neura
    • 7.2.6 Augury Systems
    • 7.2.7 Glassbeam
    • 7.2.8 Comfy
    • 7.2.9 mnubo
    • 7.2.10 C-B4
    • 7.2.11 PointGrab
    • 7.2.12 Tellmeplus
    • 7.2.13 Moov
    • 7.2.14 Sentenai
    • 7.2.15 Imagimob
    • 7.2.16 FocusMotion
    • 7.2.17 MoBagel

8 Conclusions and Recommendations

  • 8.1 Recommendations for Data Analytics Providers
  • 8.2 Recommendations for AI and Machine Learning Companies
  • 8.3 Recommendations for IoT Companies and Equipment Manufacturers
  • 8.4 Recommendations for Service Providers
  • 8.5 Recommendations for Enterprise

Figures

  • Figure 1: Artificial Intelligence Technology
  • Figure 2: Sentiment Analysis with Deep Machine Learning
  • Figure 3: Artificial Intelligence (AI) and Predictive Layer System
  • Figure 4: Industrial IoT Landscape and Machine Learning as a Service (MLaaS)
  • Figure 5: Machine Learning Use Case Scenario
  • Figure 6: Artificial Intelligence and Machine Learning Landscape and Impact
  • Figure 7: Global AI Powered Predictive Analytics Market 2016 - 2021
  • Figure 8: AI Powered Analytics Driven Productivity Gains in Industry Verticals 2016 - 2021
  • Figure 9: AI Driven System and Hardware Market 2016 - 2021
  • Figure 10: AI Driven Cognitive Computing Market 2016 - 2021
  • Figure 11: Business Content Created by AI Powered Smart Machines 2018 - 2021
  • Figure 12: Worker Supervision by AI Powered Robo-Boss 2018 - 2021
  • Figure 13: Building Security System by AI Capabilities 2018 - 2021
  • Figure 14: Business Analytics Software by AI Powered Predictive Analytics 2018 - 2021
  • Figure 15: AI powered Smart Machines vs. Humans 2018 - 2021
  • Figure 16: Global AI Connected Devices Deployment 2016 - 2021

Tables

  • Table 1: Global AI Powered Predictive Analytics Market by Type 2016 - 2021
  • Table 2: Global AI Powered Predictive Analytics Market by Business Application 2016 - 2021
  • Table 3: Global AI Powered Predictive Analytics Market by Core Technology 2016 - 2021
  • Table 4: Global AI Powered Predictive Analytics Market by Technology Applications 2016 - 2021
  • Table 5: Global AI Powered Predictive Analytics Market by Segment 2016 - 2021
  • Table 6: Global AI Powered Predictive Analytics Market by Industry Verticals 2016 - 2021
  • Table 7: Global Economic Transaction by AI Powered Autonomous Software Agents 2018 - 2021
  • Table 8: AI Self Service Visual Discovery and Data Penetration Tools 2018 - 2021
  • Table 9: AI Powered Predictive Analytics Market by Region 2016 - 2021
  • Table 10: North America AI Powered Predictive Analytics Market by Type 2016 - 2021
  • Table 11: North America AI Powered Predictive Analytics Market by Business Application 2016 - 2021
  • Table 12: North America AI Powered Predictive Analytics Market by Core Technology 2016 - 2021
  • Table 13: North America AI Powered Predictive Analytics Market by Technology App 2016 - 2021
  • Table 14: North America AI Powered Smart & Predictive Analytics Market by Segment 2016 - 2021
  • Table 15: North America AI Powered Predictive Analytics Market by Industry Vertical 2016 - 2021
  • Table 16: Europe AI Powered Predictive Analytics Market by Type 2016 - 2021
  • Table 17: Europe AI Powered Predictive Analytics Market by Business Application 2016 - 2021
  • Table 18: Europe AI Powered Predictive Analytics Market by Core Technology 2016 - 2021
  • Table 19: Europe AI Powered Predictive Analytics Market by Technology Application 2016 - 2021
  • Table 20: Europe AI Powered Predictive Analytics Market by Segment 2016 - 2021
  • Table 21: Europe AI Powered Predictive Analytics Market by Industry Vertical 2016 - 2021
  • Table 22: APAC AI Powered Predictive Analytics Market by Type 2016 - 2021
  • Table 23: APAC AI Powered Predictive Analytics Market by Business Application 2016 - 2021
  • Table 24: APAC AI Powered Predictive Analytics Market by Core Technology 2016 - 2021
  • Table 25: APAC AI Powered Predictive Analytics Market by Technology Applications 2016 - 2021
  • Table 26: APAC AI Powered Predictive Analytics Market by Segment 2016 - 2021
  • Table 27: APAC AI Powered Predictive Analytics Market by Industry Vertical 2016 - 2021
  • Table 28: ME&A AI Powered Predictive Analytics Market by Type 2016 - 2021
  • Table 29: ME&A AI Powered Predictive Analytics Market by Business Application 2016 - 2021
  • Table 30: ME&A AI Powered Predictive Analytics Market by Core Technology 2016 - 2021
  • Table 31: ME&A AI Powered Predictive Analytics Market by Technology Application 2016 - 2021
  • Table 32: ME&A AI Powered Predictive Analytics Market by Segment 2016 - 2021
  • Table 33: ME&A AI Powered Predictive Analytics Market by Industry Vertical 2016 - 2021
  • Table 34: Latin America AI Powered Predictive Analytics Market by Type 2016 - 2021
  • Table 35: Latin America AI Powered Predictive Analytics Market by Business Application 2016 - 2021
  • Table 36: Latin America AI Powered Predictive Analytics Market by Core Technology 2016 - 2021
  • Table 37: Latin America AI Powered Predictive Analytics Market by Technology App 2016 - 2021
  • Table 38: Latin America AI Powered Predictive Analytics Market by Segment 2016 - 2021
  • Table 39: Latin America AI Powered Predictive Analytics Market by Industry Vertical 2016 - 2021
  • Table 40: Global Device Deployment by Device Type and Platform 2016 - 2021
  • Table 41: Global Device Deployment by Application Sector 2016 - 2021
  • Table 42: Global Device Deployment by Core Technology 2016 - 2021
  • Table 43: Global Device Deployment by Technology Application 2016 - 2021
  • Table 44: Global Device Deployment by Segment 2016 - 2021
  • Table 45: Global Device Deployment by Industry Vertical 2016 - 2021
  • Table 46: AI Connected Device Deployment by Region 2016 - 2021
  • Table 47: North America Device Deployment by Device Type and Platform 2016 - 2021
  • Table 48: North America Device Deployment by Application Sector 2016 - 2021
  • Table 49: North America Device Deployment by Core Technology 2016 - 2021
  • Table 50: North America Device Deployment by Technology Application 2016 - 2021
  • Table 51: North America Device Deployment by Segment 2016 - 2021
  • Table 52: North America Device Deployment by Industry Vertical 2016 - 2021
  • Table 53: Europe Device Deployment by Device Type and Platform 2016 - 2021
  • Table 54: Europe AI Device Deployment by Application Sector 2016 - 2021
  • Table 55: Europe Device Deployment by Core Technology 2016 - 2021
  • Table 56: Europe Device Deployment by Technology Application 2016 - 2021
  • Table 57: Europe Device Deployment by Segment 2016 - 2021
  • Table 58: Europe Device Deployment by Industry Vertical 2016 - 2021
  • Table 59: APAC AI Device Deployment by Device Type and Platform 2016 - 2021
  • Table 60: APAC Device Deployment by Application Sector 2016 - 2021
  • Table 61: APAC Device Deployment by Core Technology 2016 - 2021
  • Table 62: APAC Device Deployment by Technology Application 2016 - 2021
  • Table 63: APAC Device Deployment by Segment 2016 - 2021
  • Table 64: APAC Device Deployment by Industry Vertical 2016 - 2021
  • Table 65: ME&A Device Deployment by Device Type and Platform 2016 - 2021
  • Table 66: ME&A Device Deployment by Application Sector 2016 - 2021
  • Table 67: ME&A Device Deployment by Core Technology 2016 - 2021
  • Table 68: ME&A Device Deployment by Technology Application 2016 - 2021
  • Table 69: ME&A Device Deployment by Segment 2016 - 2021
  • Table 70: ME&A Device Deployment by Industry Vertical 2016 - 2021
  • Table 71: Latin America Device Deployment by Device Type and Platform 2016 - 2021
  • Table 72: Latin America Devices Deployment by Application Sector 2016 - 2021
  • Table 73: Latin America Device Deployment by Application Sector 2016 - 2021
  • Table 74: Latin America Device Deployment by Technology Application 2016 - 2021
  • Table 75: Latin America Device Deployment by Segment 2016 - 2021
  • Table 76: Latin America Device Deployment by Industry Vertical 2016 - 2021
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