市場調査レポート
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1117062

サービスとしての機械学習(MLaaS)の世界市場予測(~2028年):コンポーネント、展開、組織サイズ、地域別の分析

Machine Learning as a Service (MLaaS) Market Forecasts to 2028 - Global Analysis By Component (Software Tools, Services), Deployment (Cloud, On Premise), Organization Size (Small and Medium Enterprises, Large Enterprises), and By Geography

出版日: | 発行: Stratistics Market Research Consulting | ページ情報: 英文 200+ Pages | 納期: 2~3営業日

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サービスとしての機械学習(MLaaS)の世界市場予測(~2028年):コンポーネント、展開、組織サイズ、地域別の分析
出版日: 2022年08月01日
発行: Stratistics Market Research Consulting
ページ情報: 英文 200+ Pages
納期: 2~3営業日
  • 全表示
  • 概要
  • 図表
  • 目次
概要

世界のサービスとしての機械学習(MLaaS)の市場規模は、2022年に33億4,000万米ドルとなり、予測期間中に38.0%のCAGRで拡大し、2028年までに230億4,000万米ドルに達すると予測されています。

当レポートでは世界のサービスとしての機械学習(MLaaS)市場を調査し、市場の促進要因・抑制要因、市場機会、COVID-19の影響、セグメント別の市場分析、競合情勢、主要企業のプロファイルなど、体系的な情報を提供しています。

目次

第1章 エグゼクティブサマリー

第2章 序文

第3章 市場動向分析

  • 促進要因
  • 抑制要因
  • 機会
  • 脅威
  • 用途分析
  • エンドユーザー分析
  • 新興市場
  • COVID-19の影響

第4章 ポーターのファイブフォース分析

第5章 世界のサービスとしての機械学習(MLaaS)市場:コンポーネント別

  • ソフトウェアツール
    • データの保存とアーカイブ
    • モデラーとプロセッシング
  • サービス
    • プロフェッショナルサービス
    • マネージドサービス

第6章 世界のサービスとしての機械学習(MLaaS)市場:展開別

  • クラウド
    • パブリッククラウド
    • プライベートクラウド
  • オンプレミス

第7章 世界のサービスとしての機械学習(MLaaS)市場:組織規模別

  • 中小企業
  • 大企業

第8章 世界のサービスとしての機械学習(MLaaS)市場:用途別

  • 拡張現実と仮想現実
  • 自動ネットワーク管理
  • コンピュータビジョン
  • 不正検出とリスク分析
  • マーケティングと広告
  • 自然言語処理
  • ネットワーク分析と自動トラフィック管理
  • 予知保全
  • セキュリティと監視

第9章 世界のサービスとしての機械学習(MLaaS)市場:エンドユーザー別

  • 航空宇宙と防衛
  • 自動化と輸送
  • 自動車
  • 銀行、金融サービス、保険
  • Eコマース
  • 教育
  • エネルギーとユーティリティ
  • 原料とユーティリティ
  • 政府
  • ヘルスケアとライフサイエンス
  • ITとテレコム
  • 製造業
  • メディアとエンターテイメント
  • 小売
  • 旅行とホスピタリティ

第10章 サービスとしての世界機械学習(MLaaS)市場:地域別

  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • 英国
    • イタリア
    • フランス
    • スペイン
    • その他欧州
  • アジア太平洋地域
    • 日本
    • 中国
    • インド
    • オーストラリア
    • ニュージーランド
    • 韓国
    • その他アジア太平洋地域
  • 南米
    • アルゼンチン
    • ブラジル
    • チリ
    • その他南米
  • 中東とアフリカ
    • サウジアラビア
    • アラブ首長国連邦
    • カタール
    • 南アフリカ
    • その他中東とアフリカ

第11章 主な発展

  • 契約、パートナーシップ、コラボレーション、合弁事業
  • 買収と合併
  • 新製品の発売
  • 拡張
  • その他の主な戦略

第12章 企業プロファイル

  • Amazon Web Services, Inc.
  • AT&T Intellectual Property
  • Claire Global
  • DeepMind Technologies Limited
  • Fair Isaac Corporation
  • Figure Eight Federal Inc
  • Google LLC
  • Hewlett Packard Enterprise Development LP
  • Hyundai Motor Company
  • IBM Corporation
  • Microsoft Corporation
  • PurePredictive, Inc,
  • SAS Institute Inc.
  • Yottamine Analytics Inc
図表

List of Tables

  • Table 1 Global Machine Learning as a Service (MLaaS) Market Outlook, By Region (2020-2028) ($MN)
  • Table 2 Global Machine Learning as a Service (MLaaS) Market Outlook, By Component (2020-2028) ($MN)
  • Table 3 Global Machine Learning as a Service (MLaaS) Market Outlook, By Software Tools (2020-2028) ($MN)
  • Table 4 Global Machine Learning as a Service (MLaaS) Market Outlook, By Data Storage and Archiving (2020-2028) ($MN)
  • Table 5 Global Machine Learning as a Service (MLaaS) Market Outlook, By Modeler and Processing (2020-2028) ($MN)
  • Table 6 Global Machine Learning as a Service (MLaaS) Market Outlook, By Services (2020-2028) ($MN)
  • Table 7 Global Machine Learning as a Service (MLaaS) Market Outlook, By Professional Services (2020-2028) ($MN)
  • Table 8 Global Machine Learning as a Service (MLaaS) Market Outlook, By Managed Services (2020-2028) ($MN)
  • Table 9 Global Machine Learning as a Service (MLaaS) Market Outlook, By Deployment (2020-2028) ($MN)
  • Table 10 Global Machine Learning as a Service (MLaaS) Market Outlook, By Cloud (2020-2028) ($MN)
  • Table 11 Global Machine Learning as a Service (MLaaS) Market Outlook, By Public Cloud (2020-2028) ($MN)
  • Table 12 Global Machine Learning as a Service (MLaaS) Market Outlook, By Private Cloud (2020-2028) ($MN)
  • Table 13 Global Machine Learning as a Service (MLaaS) Market Outlook, By On Premise (2020-2028) ($MN)
  • Table 14 Global Machine Learning as a Service (MLaaS) Market Outlook, By Organization Size (2020-2028) ($MN)
  • Table 15 Global Machine Learning as a Service (MLaaS) Market Outlook, By Small and Medium Enterprises (2020-2028) ($MN)
  • Table 16 Global Machine Learning as a Service (MLaaS) Market Outlook, By Large Enterprises (2020-2028) ($MN)
  • Table 17 Global Machine Learning as a Service (MLaaS) Market Outlook, By Application (2020-2028) ($MN)
  • Table 18 Global Machine Learning as a Service (MLaaS) Market Outlook, By Augmented & Virtual Reality (2020-2028) ($MN)
  • Table 19 Global Machine Learning as a Service (MLaaS) Market Outlook, By Automated Network Management (2020-2028) ($MN)
  • Table 20 Global Machine Learning as a Service (MLaaS) Market Outlook, By Computer Vision (2020-2028) ($MN)
  • Table 21 Global Machine Learning as a Service (MLaaS) Market Outlook, By Fraud Detection and Risk Analytics (2020-2028) ($MN)
  • Table 22 Global Machine Learning as a Service (MLaaS) Market Outlook, By Marketing and Advertisement (2020-2028) ($MN)
  • Table 23 Global Machine Learning as a Service (MLaaS) Market Outlook, By Natural Language Processing (2020-2028) ($MN)
  • Table 24 Global Machine Learning as a Service (MLaaS) Market Outlook, By Network Analytics and Automated Traffic Management (2020-2028) ($MN)
  • Table 25 Global Machine Learning as a Service (MLaaS) Market Outlook, By Predictive Maintenance (2020-2028) ($MN)
  • Table 26 Global Machine Learning as a Service (MLaaS) Market Outlook, By Security & Surveillance (2020-2028) ($MN)
  • Table 27 Global Machine Learning as a Service (MLaaS) Market Outlook, By End User (2020-2028) ($MN)
  • Table 28 Global Machine Learning as a Service (MLaaS) Market Outlook, By Aerospace and Defense (2020-2028) ($MN)
  • Table 29 Global Machine Learning as a Service (MLaaS) Market Outlook, By Automation and Transportation (2020-2028) ($MN)
  • Table 30 Global Machine Learning as a Service (MLaaS) Market Outlook, By Automotive (2020-2028) ($MN)
  • Table 31 Global Machine Learning as a Service (MLaaS) Market Outlook, By Banking, Financial Services and Insurance (BFSI) (2020-2028) ($MN)
  • Table 32 Global Machine Learning as a Service (MLaaS) Market Outlook, By E-Commerce (2020-2028) ($MN)
  • Table 33 Global Machine Learning as a Service (MLaaS) Market Outlook, By Education (2020-2028) ($MN)
  • Table 34 Global Machine Learning as a Service (MLaaS) Market Outlook, By Energy & Utilities (2020-2028) ($MN)
  • Table 35 Global Machine Learning as a Service (MLaaS) Market Outlook, By Feedstock & Utilities (2020-2028) ($MN)
  • Table 36 Global Machine Learning as a Service (MLaaS) Market Outlook, By Government (2020-2028) ($MN)
  • Table 37 Global Machine Learning as a Service (MLaaS) Market Outlook, By Healthcare & Life Sciences (2020-2028) ($MN)
  • Table 38 Global Machine Learning as a Service (MLaaS) Market Outlook, By IT and Telecom (2020-2028) ($MN)
  • Table 39 Global Machine Learning as a Service (MLaaS) Market Outlook, By Manufacturing (2020-2028) ($MN)
  • Table 40 Global Machine Learning as a Service (MLaaS) Market Outlook, By Media and Entertainment (2020-2028) ($MN)
  • Table 41 Global Machine Learning as a Service (MLaaS) Market Outlook, By Retail (2020-2028) ($MN)
  • Table 42 Global Machine Learning as a Service (MLaaS) Market Outlook, By Travel & Hospitality (2020-2028) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.

目次
Product Code: SMRC21729

According to Stratistics MRC, the Global Machine Learning as a Service (MLaaS) Market is accounted for $3.34 billion in 2022 and is expected to reach $23.04 billion by 2028 growing at a CAGR of 38.0% during the forecast period. Machine learning as a service incorporates a comprehensive range of services and solutions and techniques interrelated closely to artificial intelligence which performs statistical analysis of input data to know its current or future relationship and performance. With massive digital transformations and technological disruptions being witnessed across diverse industry users, machine learning technology is surprising even the technology experts with new commercially viable use cases and a plethora of diverse industrial applications that it brings along with it. Machine learning as a service (MLaaS) incorporates range of services that offer machine learning tools through cloud computing services.

The global artificial intelligence market, currently valued at 327.5 billion U.S. dollars, continues to grow driven by the influx of investments it receives. From 2015 to 2020, the total yearly corporate global investment in AI increased by 55 billion U.S. dollars, with much of it coming from private investments from U.S. companies. The most recent top funded artificial intelligence startups in the United States are that of UiPath, Nuro, and Indigo Ag to name a few. UiPath is the AI startup to watch, as it is considered the second most valuable AI unicorn startup worldwide at 35 billion U.S. dollars and has accomplished the designation of the most popular robotic process automation (RPA) product vendor across Global 2000 enterprises.

Market Dynamics:

Driver:

Rapid advancements in technologies

With advancements in data science and artificial intelligence, the performance of machine learning accelerated at a rapid pace. Companies are offering machine learning solutions on a subscription-based model, making it easier for consumers to take advantage of this technology. In addition, it provides flexibility on a pay-as-you-use basis. Companies are now identifying the potential of this technology, and therefore, the adoption rate of the same is expected to increase over the forecast period. MLaaS products offered by companies are micro services offered by significant cloud computing firms like Microsoft Azure, Google Cloud Platform, and Amazon Web Services. These solutions typically include pre-built natural language processing (NLP), computer vision, and general machine learning algorithms.

Restraint:

Possibility of high error

In machine learning as a service (MLaaS), the user can choose the algorithms based on accurate results. For that, they have to run the results on every algorithm. The main problem occurs in the training and testing of data. The data is huge, so sometimes removing errors becomes nearly impossible. These errors can cause a headache to users. Since the data is huge, the errors take a lot of time to resolve.

Opportunity:

Increase in data from IoT platforms

IoT operations ensure that the thousands or more devices run correctly and safely on an enterprise network, and the data that is being collected is both timely and accurate. Machine learning could engage in demystifying the hidden patterns in IoT data by analyzing significant volumes of data utilizing sophisticated algorithms. ML inference could supplement or replace manual processes with automated systems utilizing statistically derived actions in critical processes. Solutions built on ML automate the IoT data modeling process, thus, removing the circuitous and labor-intensive activities of model selection, coding, and validation. While the sophisticated back-end analytics engines work on the heavy lifting of processing the stream of data, ensuring the quality of the data is often left to obsolete methodologies. To ensure the rein in sprawling IoT infrastructures, some IoT platform vendors are baking machine learning technology to boost their operations management capabilities. Small businesses adopting IoT could significantly save on the time-consuming process of machine learning. As enterprises increasingly adopt IoT-based technologies and solutions, more companies leverage machine learning technologies for data analytics. Therefore, the MLaaS is anticipated to drive innovation in IoT.

Threat:

Lack of skilled consultants

The lack of skilled consultants to deploy machine learning services is restraining the growth of machine learning as a service market. Since machine learning revolves around algorithms, model complexity, and computational complexity, it requires skilled professionals to develop these solutions. Several of the machine learning based offerings for predictive analytics are deployed to support an industry or a domain-specific usage scenario. Integration of machine learning services can be done through both software and services depending on the level and nature of integration. Professional services of a data scientist or a developer are needed to customize an existing machine learning service, which caters to an industry. Moreover, enterprises need professional services to customize a particular capability to implement on their MLaaS platform.

COVID-19 Impact

The COVID-19 is expected to positively impact the growth of the machine learning (ML) market during the analysis period. This is attributed to the significant acceleration in the adoption of ML technology in healthcare, automotive, and retail, among others. The COVID-19 pandemic has significantly affected country's health, financial, and social systems. The application of artificial intelligence technology is likely to help combat the COVID-19 pandemic. Several countries are using population surveillance methods to track and trace COVID-19 cases. For instance, according to The Brookings Institution, in South Korea, researchers use surveillance camera footage and geolocation data to track coronavirus patients. The data scientists use this surveillance camera footage data and with the help of machine intelligence algorithms they predict the location of the next outbreak and inform the responsible authorities, to track the COVID positive patients in real-time. Such active initiatives are likely to surge the demand for machine intelligence solutions in the upcoming period.

The network analytics and automated traffic management segment is expected to be the largest during the forecast period

The network analytics and automated traffic management segment is estimated to have a lucrative growth due to the exceptional growth of data across verticals. Machine learning is considered as a pivotal tool for network analytics and automated traffic management. Large amounts of data traverse network infrastructure on an everyday basis. By using general low overhead sensors in both hardware and software, an entire understanding of application and network performance can be achieved dynamically. With the advent of big data analytics, it has become possible to apply network-rich metrics to supply unmatched understanding into the IT infrastructure.

The small and medium enterprises segment is expected to have the highest CAGR during the forecast period

The small and medium enterprises segment is anticipated to witness the fastest CAGR growth during the forecast period. Small and medium enterprises prefer MLaaS as the data provided by the machine learning application is dynamic. Small and medium enterprises can use machine learning solutions for the fine-tuning of their supply chain by predicting a product demand and providing suggestions on the timing and quantity of supplies required in order to meet customers' expectations. With the help of predictive analytics machine learning algorithms not only give real time data but also predict the future instances.

Region with largest share:

North America is projected to hold the largest market share during the forecast period. North American region is foremost in deploying machine learning services into many applications and domains. North America has been the most forward towards adopting Machine Learning Services. In addition, this region has been extremely responsive towards adopting the latest technological advancements such as integration technologies with cloud, Big Data within Machine Learning Services.

Region with highest CAGR:

Asia Pacific is projected to have the highest CAGR over the forecast period due to the increasing awareness and sustainable growth of IT sector in the region. Positive growth and development of IoT technology sector in the region is expected to swell the demand for the machine learning as a service. Apart from this, rising adoption of advanced analytics tools in healthcare is expected to fuel the growth of machine learning as a service market in the Asia Pacific region.

Key players in the market:

Some of the key players profiled in the Machine Learning as a Service (MLaaS) Market include Amazon Web Services, Inc., AT&T Intellectual Property, Claire Global, DeepMind Technologies Limited, Fair Isaac Corporation, Figure Eight Federal Inc, Google LLC, Hewlett Packard Enterprise Development LP, Hyundai Motor Company, IBM Corporation, Microsoft Corporation, PurePredictive, Inc, SAS Institute Inc., and Yottamine Analytics Inc.

Key Developments:

In April 2021, Microsoft Corporation has announced an open Dataset for transportation, health and genomics, labour and economics, population and safety, supplemental and common datasets to improve accuracy of machine learning models with publicly available datasets.

In June 2021, Hyundai Motor Company has been heavily investing human and material resources in the race to develop self-driving cars. Hyundai Motor Company significantly accelerated model training using the scalable AWS Cloud and Amazon SageMaker, including the new SageMaker library for data parallelism.

In April 2021, Claire Global has leveraged a purpose-built machine learning solution to optimize the buying and selling processes to increase customer conversion and customer engagement. Some of the custom features offered by the company are automated product recommendations, optimal pricing suggestions for sellers, stock management for buyers and sellers, and anomaly detection.

Components Covered:

  • Software Tools
  • Services

Deployments Covered:

  • Cloud
  • On Premise

Organization Sizes Covered:

  • Small and Medium Enterprises
  • Large Enterprises

Applications Covered:

  • Augmented & Virtual Reality
  • Automated Network Management
  • Computer Vision
  • Fraud Detection and Risk Analytics
  • Marketing and Advertisement
  • Natural Language Processing
  • Network Analytics and Automated Traffic Management
  • Predictive Maintenance
  • Security & Surveillance

End Users Covered:

  • Aerospace and Defense
  • Automation and Transportation
  • Automotive
  • Banking, Financial Services and Insurance (BFSI)
  • E-Commerce
  • Education
  • Energy & Utilities
  • Feedstock & Utilities
  • Government
  • Healthcare & Life Sciences
  • IT and Telecom
  • Manufacturing
  • Media and Entertainment
  • Retail
  • Travel & Hospitality

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2020, 2021, 2022, 2025, and 2028
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Application Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Machine Learning as a Service (MLaaS) Market, By Component

  • 5.1 Introduction
  • 5.2 Software Tools
    • 5.2.1 Data Storage and Archiving
    • 5.2.2 Modeler and Processing
  • 5.3 Services
    • 5.3.1 Professional Services
    • 5.3.2 Managed Services

6 Global Machine Learning as a Service (MLaaS) Market, By Deployment

  • 6.1 Introduction
  • 6.2 Cloud
    • 6.2.1 Public Cloud
    • 6.2.2 Private Cloud
  • 6.3 On Premise

7 Global Machine Learning as a Service (MLaaS) Market, By Organization Size

  • 7.1 Introduction
  • 7.2 Small and Medium Enterprises
  • 7.3 Large Enterprises

8 Global Machine Learning as a Service (MLaaS) Market, By Application

  • 8.1 Introduction
  • 8.2 Augmented & Virtual Reality
  • 8.3 Automated Network Management
  • 8.4 Computer Vision
  • 8.5 Fraud Detection and Risk Analytics
  • 8.6 Marketing and Advertisement
  • 8.7 Natural Language Processing
  • 8.8 Network Analytics and Automated Traffic Management
  • 8.9 Predictive Maintenance
  • 8.10 Security & Surveillance

9 Global Machine Learning as a Service (MLaaS) Market, By End User

  • 9.1 Introduction
  • 9.2 Aerospace and Defense
  • 9.3 Automation and Transportation
  • 9.4 Automotive
  • 9.5 Banking, Financial Services and Insurance (BFSI)
  • 9.6 E-Commerce
  • 9.7 Education
  • 9.8 Energy & Utilities
  • 9.9 Feedstock & Utilities
  • 9.10 Government
  • 9.11 Healthcare & Life Sciences
  • 9.12 IT and Telecom
  • 9.13 Manufacturing
  • 9.14 Media and Entertainment
  • 9.15 Retail
  • 9.16 Travel & Hospitality

10 Global Machine Learning as a Service (MLaaS) Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 Amazon Web Services, Inc.
  • 12.2 AT&T Intellectual Property
  • 12.3 Claire Global
  • 12.4 DeepMind Technologies Limited
  • 12.5 Fair Isaac Corporation
  • 12.6 Figure Eight Federal Inc
  • 12.7 Google LLC
  • 12.8 Hewlett Packard Enterprise Development LP
  • 12.9 Hyundai Motor Company
  • 12.10 IBM Corporation
  • 12.11 Microsoft Corporation
  • 12.12 PurePredictive, Inc,
  • 12.13 SAS Institute Inc.
  • 12.14 Yottamine Analytics Inc