Machine Learning as a Service (MLaaS) Market

Machine Learning as a Service (MLaaS) Market by Component (Software Tools, Services), Organization Size, Application (Marketing, Risk Analytics & Fraud Detection, Predictive Maintenance, Network Analytics), Service and Region - Global Forecast to 2021

Report Code: TC 4696 Nov, 2016, by marketsandmarkets.com

[137 Pages Report] The Machine Learning as a Service (MLaaS) market is estimated to grow from USD 613.4 million in 2016 to USD 3755.0 million by 2021, at a Compound Annual Growth Rate (CAGR) of 43.7% during the forecast period. The base year considered for the study is 2015 and the forecast period is from 2016 to 2021. Adoption of cloud based technologies, strong need to understand customer behavior, and advancements in technologies is expected to be driving the growth of the market in the coming years.

Machine learning is a computing technology that offers computers the ability to learn and modify their analytical functionalities when exposed to new data sets, without being explicitly programmed. There are several factors that trigger the growth of the machine learning along with its associated advanced computing and analytics market. Some of them are rising demand for mapping customer behavior especially by the marketing and advertising sector, increasing concerns for security, and growing need for applications for support during emergency.

Market Dynamics

Drivers

  • Adoption of cloud-based technologies
  • Strong need to understand customer behavior
  • Advancements in technologies

Restraints

  • Government and compliance issues
  • Lack of skilled consultants to deploy machine learning services

Opportunities

  • Emerging options in application areas
  • Increasing investments in the healthcare industry    
  • Improved connectivity and increase in data from IoT platforms

Challenges

  • Need for effective predictive technologies
  • Integration into the enterprises

Advancements in technologies

Rapid advancements and innovations are happening in enabling technologies. Various solution providers are doing a lot of work in these areas. for instance, Affectiva recently launched its emotion analytics technology that has the largest data repository of over 2 million face videos, enabling its clients to achieve high accuracy with unmatchable insights. Apart from that, other players such as small players such as Cognitec System, Emotient, Gesturetek, Saffron, and Palantir are also making significant advancements in the field of gesture recognition, face recognition, cognitive computing, and neuron analytics. These developments are expected to fuel the growth of the market in coming years.

Objectives of the Study:

The main objective of this report is to define, describe, and forecast the global MLaaS market on the basis of components which includes solutions/tools, services, and service models, along with application areas and regions. The report provides detailed information regarding the major factors influencing the growth of the market (drivers, restraints, opportunities, and industry-specific challenges). The report attempts to forecast the market size with respect to five main regions, namely, North America, Europe, Asia-Pacific (APAC), Latin America, and Middle East & Africa (MEA). The report strategically profiles key players and comprehensively analyzes their core competencies. This report also tracks and analyzes competitive developments such as joint ventures, Mergers and Acquisitions (M&A), new product developments, and Research & Development (R&D) activities in the MLaaS market.

The research methodology used to estimate and forecast the MLaaS market begins with collection and analysis of data on key vendor revenues through secondary research such as annual reports and press releases, investor presentations of companies, white papers, technology journals including SDx Central, MLaaS evolution, certified publications, and articles from recognized authors, directories, and databases. The vendor offerings have also been taken into consideration to determine the market segmentation. The bottom-up procedure was employed to arrive at the total market size of the global MLaaS market from the revenue of the key solution and service providers in the market. After arriving at the overall market size, the total market was split into several segments and sub-segments, which were then verified through primary research by conducting extensive interviews with key people, such as CEOs, VPs, directors, and executives. The data triangulation and market breakdown procedures were employed to complete the overall market engineering process and to arrive at the exact statistics for all segments and sub-segments. The breakdown of profiles of primary is depicted in the below figure:

Breakdown of Primary Participants

Machine Learning as a Service (MLaaS) Market

To know about the assumptions considered for the study, download the pdf brochure

The key players considered in the MLaaS market are Microsoft (Washington, US), Amazon Web Services (Washington, US), Hewlett Packard Enterprises (California, US), Google, Inc.(California, US), BigML Inc. (Oregon, US), FICO(California, US), IBM Corporation (New York, US), AT&T (Dallas, US), Fuzzy.ai (Montreal, Canada), Yottamine Analytics (Washington, US), Ersatz Labs (California, US), Sift-Science (California, US)

Major Market Developments

  • In September 2016, Microsoft collaborated with Liebherr a Switzerland large equipment manufacturer. This collaboration has resulted in the development of a new generation of ‘SmartDeviceBox’. It is a communication module, which will fit in Liebherr’s refrigerators and freezers and will connect them to the internet
  • In August 2016, HPE announced HPE Haven On Demand (HOD) Combinations, a new cloud-based offering which is developed on HPE Haven OnDemand platform. It encourages developers to utilize the power of machine learning to develop next generation application
  • In July 2016, Google acquired a French firm Moodstocks, which is an on-device image recognition software for smartphones

Key Target Audience

  • Education.
  • Banking and Financial services.
  • Insurance.
  • Automotive and Transportation.
  • Defense.
  • Healthcare.
  • Retail and e-Commerce.
  • Media and Entertainment.
  • Telecom
  • Others(Industrial Manufacturing, Mining, Agriculture, Utilities (Water/Energy/Oil & Gas), and Hospitality)

Scope of the Report

The research report categorizes the machine learning as a service (MLaaS) market to forecast the revenues and analyze the trends in each of the following sub-segments:

By Software Tool and Services

  • Software Tools
  • Data Storage and Archiving
    • Modeler and Processing
    • Multiplayer Perceptron (MLP)
    • K-Nearest Neighbors (KMN)
    • Support Vector Regressions (SVR)
    • Others(Decision Tree(DT),
      • Principle Component Analysis(PCA), Principle Component Analysis(PCA)
      • k- Means Algorithms, Reinforcement Learning, and Bayesian Statistics))
  • Cloud and Web-based Application Programming Interface (APIs)
  • Others (Model Validator, Decision Report/Predictor/Training, and Report Storage)

By Service:

  • Professional Services
  • Managed Services

By Enterprise Applications:

  • Marketing and Advertising.
  • Risk Analytics and Fraud Detection
  • Predictive Maintenance (Pattern recognition & generation, anomaly detection)
  • Augmented Reality (Pattern recognition & generation, object recognition, automated simulation, prediction/recommendation)
  • Network Analytics and Automated Traffic Management (SDN and NFV/ automated traffic generation/ etc.)
  • Others (data classification, recommendation engines, social media and customer analytics, sales lifecycle management).

By Organization Size:

  • SMEs
  • Large Enterprises

By Region:

  • North America
  • Europe
  • Asia-Pacific (APAC)
  • Rest of the World (RoW)

Critical questions which the report answers

  • Which vertical holds the maximum opportunity of growth in the machine learning as a service market?
  • Growth strategies undertaken by the key players and innovators in the machine learning as a service market?

Available Customizations

With the given market data, MarketsandMarkets offers customizations as per the company’s specific needs. The following customization options are available for the report:

Geographic Analysis

  • Further breakdown of the Europe MLaaS market
  • Further breakdown of the APAC MLaaS market

Company Information

  • Detailed analysis and profiling of additional market players

The Machine Learning as a Service (MLaaS) market size is expected to grow from USD 613.4 million in 2016 to USD 3755.0 million by 2021, at a Compound Annual Growth Rate (CAGR) of 43.7% during the forecast period. In the report, 2015 is considered the base year while the forecast period is 2016–2021. The major growth driver for the growth of the market are adoption of cloud-based technologies, strong need to understand customer behavior, and advancements in technologies.

Machine learning is an innovative buzzword across enterprises, who are using it and technology vendors, who are developing and implementing it. With massive digital transformations and technological disruptions being witnessed across diverse industry sectors, 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

The MLaaS market is segmented by components (software tool and services), application, organization size, vertical, and region. Among the various software tools, cloud and web based API is expected to grow with the highest CAGR, as cloud and web based API can analyze data and add several application features related to machine learning algorithms, such as customer sentiment analysis, spam detection, recommendation systems, and many more.

In the services segment, professional segment is expected to hold the largest market share in the MLaaS market during the forecast period. Most of the companies do not have the expertise to successfully manage infrastructure and hence, they outsource these services to third-party partners to maintain the level of security and safety. Professional services include designing, planning, upgrades, and consulting services. The growth of the professional services segment is mainly governed by the complexity of operations and increasing deployment of manufacturing machine learning solutions. Professional services are categorized into three types, namely, deployment & integration, support & maintenance, and consulting services

By application, network analytics and automated traffic management application is expected to grow at the highest rate during the forecast period. This growth is attributed because Machine learning is considered as a pivotal tool for network analytics and automated traffic management. This is owing to exceptional growth of data across verticals. Large amounts of data traverse network infrastructure on an everyday basis. With the advent of big data analytics, it has become possible to apply network-rich metrics to supply unmatched understanding into the IT infrastructure. By using general low overhead sensors in both hardware and software, an entire understanding of application and network performance can be achieved dynamically.

By organization size SMBs segment is expected to grow at the highest CAGR in the MLaaS market during the forecast period. SMBs prefer MLaaS as the data provided by the machine learning application is dynamic. With the help of predictive analytics machine learning algorithms not only give real time data but also predict the future instances. SMBs 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.

North America is expected to contribute the largest market share and will continue to grow at the highest rate. North America has been the most forward towards adopting Machine Learning Services. Furthermore, this region has been extremely responsive towards adopting the latest technological advancements such as integration technologies with cloud, Big Data within Machine Learning Services. North America is foremost in deploying machine learning services into many applications and domains.

Machine Learning as a Service (MLaaS) Market

Strong need to understand customer behavior drives the growth of the machine learning as a service market.

Data Storage and Archiving

In deep learning algorithms, data storage and archiving software plays a vital role in predicting the solutions for very complex problems. Since a deep learning algorithm deals with an artificial neural network composed of many layers, it needs a large amount of data sets to provide the result. Deep learning algorithm uses data storage and archiving software to target the complex functions in the artificial neural network.

Modeler and Processing

Over the last decade, machine learning technologies have evolved into “algorithms” formulated from diverse fields including statistics, mathematics, neuroscience, and computer science, making them commercially viable and computationally robust. Many applications available today such as speech recognition, fraud detection, and network optimization use a variety of machine learning techniques based on classification, regression, and estimation to process structured data sets.

Cloud and Web-Based Application Programming Interface (APIS) (APIS)

In machine learning algorithm, requirement of data is an essential input parameter. Some of the business verticals such as banking and financial services need a large amount of data instantly to predict the market behavior. Machine learning algorithms get very less time to predict solutions after gathering data from data storage and archiving software. To overcome this complexity, machine learning algorithms create an interface between cloud and the application platform.

Critical questions the report answers:

  • Where will all these developments take the industry in the mid to long term?
  • What are the upcoming industry applications for machine learning as a service?

Lack of skilled consultants to deploy machine learning services is restraining the growth of machine learning as a service market. Integration of machine learning services can be done through both software and services depending on the level and nature of integration. In addition, enterprises need professional services to customize a particular capability to implement on their MLaaS platform. Several of the machine learning based offerings for predictive analytics are deployed to support an industry or a domain-specific usage scenario. Professional services of a data scientist or a developer are needed to customize an existing machine learning service, which caters to an industry.

Major vendors in the market are Microsoft (Washington,US), Amazon Web Services (Washington, US), Hewlett Packard Enterprises (California, US), Google, Inc.(California, US), BigML Inc. (Oregon, US), FICO(California, US), IBM Corporation (New York, US), AT&T (Dallas, US), Fuzzy.ai (Montreal, Canada), Yottamine Analytics (Washington, US), Ersatz Labs (California, US), Sift-Science (California, US). These vendors have adopted different types of organic and inorganic growth strategies such as new product launches, partnerships & collaborations, and mergers & acquisitions to expand their offerings in the SDP market.

To speak to our analyst for a discussion on the above findings, click Speak to Analyst

Table of Contents

1 Introduction (Page No. - 14)
    1.1 Objectives of the Study
    1.2 Market Definition
    1.3 Market Scope
           1.3.1 Markets Covered
           1.3.2 Years Considered
    1.4 Currency
    1.5 Limitations
    1.6 Stakeholders

2 Research Methodology (Page No. - 18)
    2.1 Research Data
           2.1.1 Secondary Data
                     2.1.1.1 Key Data From Secondary Sources
           2.1.2 Primary Data
                     2.1.2.1 Data From Primary Sources
                     2.1.2.2 Key Industry Insights
                     2.1.2.3 Breakdown of Primaries
    2.2  Market Size Estimation
           2.2.1 Bottom-Up Approach
           2.2.2 Top-Down Approach
    2.3 Market Breakdown and Data Triangulation
    2.4 Research Assumptions

3 Executive Summary (Page No. - 26)

4 Premium Insights (Page No. - 31)
    4.1 Attractive Opportunities in the MLaaS Market
    4.2 Market: Enterprise Applications
    4.3 Global Market, By Region and Industry Vertical
    4.4 Market Potential
    4.5 Lifecycle Analysis, By Region, 2016

5 Market Overview (Page No. - 34)
    5.1 Introduction
    5.2 Market Segmentation
           5.2.1 By Component
           5.2.2 By Organization Size
           5.2.3 By Enterprise Application
           5.2.4 By Industry Vertical
           5.2.5 By Region
    5.3 Market Dynamics
           5.3.1 Drivers
                     5.3.1.1 Adoption of Cloud-Based Technologies
                     5.3.1.2 Strong Need to Understand Customer Behavior
                     5.3.1.3 Advancements in Technologies
           5.3.2 Restraints
                     5.3.2.1 Government and Compliance Issues
                     5.3.2.2 Lack of Skilled Consultants to Deploy Machine Learning Services
           5.3.3 Opportunities
                     5.3.3.1 Emerging Options in Application Areas
                     5.3.3.2 Increasing Investments in the Healthcare Industry
                     5.3.3.3 Improved Connectivity and Increase in Data From IoT Platforms
           5.3.4 Challenges
                     5.3.4.1 Need for Effective Predictive Technologies
                     5.3.4.2 Integration Into the Enterprises

6 Industry Trends (Page No. - 43)
    6.1 Introduction
    6.2 Machine Learning as a Service: Evolution
    6.3 MLaaS : Value Chain Analysis
    6.4 Standards and Regulations
           6.4.1 Introduction
           6.4.2 Cloud Computing Association (CCA)
           6.4.3 Storage Networking Industry Association (SNIA)
           6.4.4 International Organization for Standardization
           6.4.5 International Telecommunication Union

7 Machine Learning as a Service Market Analysis, By Component (Page No. - 47)
    7.1 Introduction
    7.2 Software Tools
           7.2.1 Data Storage and Archiving
           7.2.2 Modeler and Processing
           7.2.3 Cloud and Web-Based Application Programming Interface
           7.2.4 Others
    7.3 Services
           7.3.1 Professional Services
           7.3.2 Managed Services

8 MLaaS Market Analysis, By Application (Page No. - 58)
    8.1 Introduction
    8.2 Marketing and Advertising
    8.3 Fraud Detection and Risk Analytics
    8.4 Predictive Maintenance
    8.5 Augmented Reality
    8.6 Network Analytics and Automated Traffic Management
    8.7 Others

9 Machine Learning as a Service Market Analysis, By Organization Size (Page No. - 69)
    9.1 Introduction
    9.2 Small and Medium Enterprises
    9.3 Large Enterprises

10 MLaaS Market Analysis, By Vertical (Page No. - 73)
     10.1 Introduction
     10.2 Education
     10.3 Banking and Financial Services
     10.4 Insurance
     10.5 Automotive and Transportation
     10.6 Healthcare
     10.7 Defense
     10.8 Retail and E-Commerce
     10.9 Media and Entertainment
     10.10 Telecom
     10.11 Others

11 Geographic Analysis (Page No. - 83)
     11.1 Introduction
     11.2 Geographical Segment
               11.2.1 North America
               11.2.2 Europe
               11.2.3 Asia-Pacific
               11.2.4 Rest of the World (RoW)

12 Competitive Landscape (Page No. - 100)
     12.1 Overview
     12.2 Competitive Situation and Trends
               12.2.1 New Product Launches and Enhancements
               12.2.2 Partnerships, Agreements, and Collaborations
               12.2.3 Mergers and Acquisitions
               12.2.4 Business Expansions

13 Company Profiles (Page No. - 105)
(Business Overview, Products & Services, Key Insights, Recent Developments, SWOT Analysis, MnM View)*
     13.1 Introduction
     13.2 Microsoft
     13.3 International Business Machine Corporation
     13.4 Amazon Web Services
     13.5 Google, Inc.
     13.6 BigML, Inc.
     13.7 FICO
     13.8 Hewlett-Packard Enterprise Development Lp.

*Details on Business Overview, Products & Services, Key Insights, Recent Developments, SWOT Analysis, MnM View Might Not Be Captured in Case of Unlisted Companies.

14 Key Innovators (Page No. - 126)
     14.1 AT&T
     14.2 Fuzzy.Ai
     14.3 Yottamine Analytics, LLC
     14.4 Ersatz Labs, Inc.
     14.5 Sift Science, Inc.

15 Appendix (Page No. - 129)
     15.1 Industry Experts
     15.2 Discussion Guide
     15.3 Knowledge Store: Marketsandmarkets’ Subscription Portal
     15.4 Introducing RT: Real-Time Market Intelligence
     15.5 Related Reports


List of Tables (72 Tables)

Table 1 Machine Learning as a Service Market Size, By Region, 2014–2021 (USD Billion)
Table 2 Market Size, By Component, 2014–2021 (USD Million)
Table 3 Market Size, By Software Tool, 2014–2021 (USD Million)
Table 4 Data Storage and Archiving: Market Size, By Organization Size, 2014–2021 (USD Million)
Table 5 Data Storage and Archiving: Market Size, By Region, 2014–2021 (USD Million)
Table 6 Modeler and Processing: Market Size, By Organization Size, 2014–2021 (USD Million)
Table 7 Modeler and Processing: MLaaS Market Size, By Region, 2014–2021 (USD Million)
Table 8 Cloud and Web-Based Application Programming Interfaces: Market Size, By Organization Size, 2014–2021 (USD Million)
Table 9 Cloud and Web-Based Application Programming Interfaces: Market Size, By Region, 2014–2021 (USD Million)
Table 10 Others: Machine Learning as a Service Market Size, By Organization Size, 2014–2021 (USD Million)
Table 11 Others: Market Size, By Region, 2014–2021 (USD Million)
Table 12 Market Size, By Service, 2014–2021 (USD Million)
Table 13 Professional Services: Market Size, By Organization Size, 2014–2021 (USD Million)
Table 14 Professional Services: Market Size, By Region, 2014–2021 (USD Million)
Table 15 Managed Services: Market Size, By Organization Size, 2014–2021 (USD Million)
Table 16 Managed Services: Market Size, By Region, 2014–2021 (USD Million)
Table 17 MLaaS Market Size, By Enterprise Application, 2014–2021 (USD Million)
Table 18 Marketing and Advertising: Market Size, By Vertical, 2014–2021 (USD Million)
Table 19 Marketing and Advertising: Market Size, By Region, 2014–2021 (USD Million)
Table 20 Fraud Detection and Risk Analytics: Market Size, By Vertical, 2014–2021 (USD Million)
Table 21 Fraud Detection and Risk Analytics: Market Size, By Region, 2014–2021 (USD Million)
Table 22 Predictive Maintenance: Machine Learning as a Service Market Size, By Vertical, 2014–2021 (USD Million)
Table 23 Predictive Maintenance: Market Size, By Region, 2014–2021 (USD Million)
Table 24 Augmented Reality: Market Size, By Vertical, 2014–2021 (USD Million)
Table 25 Augmented Reality: Market Size, By Region, 2014–2021 (USD Million)
Table 26 Network Analytics and Automated Traffic Management: MLaaS Market Size, By Vertical, 2014–2021 (USD Million)
Table 27 Network Analytics and Automated Traffic Management: Market Size, By Region, 2014–2021 (USD Million)
Table 28 Others: Market Size, By Vertical, 2014–2021 (USD Million)
Table 29 Others: Market Size, By Region, 2014–2021 (USD Million)
Table 30 Market Size, By Organization Size, 2014–2021 (USD Million)
Table 31 Small and Medium Enterprises: Machine Learning as a Service Market Size, By Region, 2014–2021 (USD Million)
Table 32 Large Enterprises: Market Size, By Region, 2014–2021 (USD Million)
Table 33 Market Size, By Vertical, 2014–2021 (USD Million)
Table 34 Education: Market Size, By Region, 2014–2021 (USD Million)
Table 35 Banking and Financial Services: Market Size, By Region, 2014–2021 (USD Million)
Table 36 Insurance: MLaaS Market Size, By Region, 2014–2021 (USD Million)
Table 37 Automotive and Transportation: Market Size, By Region, 2014–2021 (USD Million)
Table 38 Healthcare: Market Size, By Region, 2014–2021 (USD Million)
Table 39 Defense: Market Size, By Region, 2014–2021 (USD Million)
Table 40 Retail and E-Commerce: Machine Learning as a Service Market Size, By Region, 2014–2021 (USD Million)
Table 41 Media and Entertainment: Market Size, By Region, 2014–2021 (USD Million)
Table 42 Telecom: Market Size, By Region, 2014–2021 (USD Million)
Table 43 Others: Market Size, By Region, 2014–2021 (USD Million)
Table 44 Market Size, By Region, 2014–2021 (USD Million)
Table 45 North America: MLaaS Market Size, By Component, 2014–2021 (USD Million)
Table 46 North America: Market Size, By Software Tool, 2014–2021 (USD Million)
Table 47 North America: Market Size, By Service, 2014–2021 (USD Million)
Table 48 North America: Market Size, By Organization Size, 2014–2021 (USD Million)
Table 49 North America: Market Size, By Enterprise Application, 2014–2021 (USD Million)
Table 50 North America: Market Size, By Vertical, 2014–2021 (USD Million)
Table 51 Europe: Machine Learning as a Service Market Size, By Component, 2014–2021 (USD Million)
Table 52 Europe: Market Size, By Software Tool, 2014–2021 (USD Million)
Table 53 Europe: Market Size, By Service, 2014–2021 (USD Million)
Table 54 Europe: Market Size, By Organization Size, 2014–2021 (USD Million)
Table 55 Europe: Market Size, By Enterprise Application, 2014–2021 (USD Million)
Table 56 Europe: Market Size, By Vertical, 2014–2021 (USD Million)
Table 57 Asia-Pacific: MLaaS Market Size, By Component, 2014–2021 (USD Million)
Table 58 Asia-Pacific: Market Size, By Software Tool, 2014–2021 (USD Million)
Table 59 Asia-Pacific: Market Size, By Service, 2014–2021 (USD Million)
Table 60 Asia-Pacific: Market Size, By Organization Size, 2014–2021 (USD Million)
Table 61 Asia-Pacific: Market Size, By Enterprise Application, 2014–2021 (USD Million)
Table 62 Asia-Pacific: Market Size, By Vertical, 2014–2021 (USD Million)
Table 63 Rest of the World: Machine Learning as a Service Market Size, By Component, 2014–2021 (USD Million)
Table 64 Rest of the World: Market Size, By Software Tool, 2014–2021 (USD Million)
Table 65 Rest of the World: Market Size, By Service, 2014–2021 (USD Million)
Table 66 Rest of the World: Market Size, By Organization Size, 2014–2021 (USD Million)
Table 67 Rest of the World: Market Size, By Enterprise Application, 2014–2021 (USD Million)
Table 68 Rest of the World: Market Size, By Vertical, 2014–2021 (USD Million)
Table 69 New Product Launches and Enhancements, 2014–2016
Table 70 Partnerships, Agreements, and Collaborations, 2014–2016
Table 71 Mergers and Acquisitions, 2014–2016
Table 72 Business Expansions, 2014–2016
 
 
List of Figures (51 Figures)
 
Figure 1 Markets Covered
Figure 2 Global Machine Learning as a Service Market: Research Design
Figure 3 Market Size Estimation Methodology: Bottom-Up Approach
Figure 4 Market Size Estimation Methodology: Top-Down Approach
Figure 5 Market Breakdown and Data Triangulation
Figure 6 North America is Expected to Lead the Adoption Trend for Machine Learning as a Service Market
Figure 7 Top Enterprise Applications for Market in 2016
Figure 8 Market Snapshot (2016–2021): Software Tools and Services
Figure 9 Market Snapshot (2015 vs 2020): Software Tools
Figure 10 Market Snapshot (2015 vs 2020): Services
Figure 11 Cloud-Based Open-Source Platform is Driving the Machine Learning as a Service Market
Figure 12 Network Analytics AMD Automated Traffic Management is Expected to Grow at the Highest CAGR During the Forecast Period
Figure 13 Retail and E-Commerce Vertical is Expected to Hold the Largest Market Share in the Machine Learning as a Service Market
Figure 14 North America is Expected to Have the Highest Growth Potential During the Forecast Period
Figure 15 Regional Lifecycle: Asia-Pacific is Expected to Be in the Growth Phase in 2015
Figure 16 Market Segmentation: By Component
Figure 17 Market Segmentation: By Organization Size
Figure 18 Market Segmentation: By Enterprise Application
Figure 19 Market Segmentation: By Industry Vertical
Figure 20 Market Segmentation: By Region
Figure 21 Market: Drivers, Restraints, Opportunities, and Challenges
Figure 22 MLaaS Evolution From 1950 to 2015
Figure 23 Value Chain Analysis
Figure 24 Machine Learning as a Service: Component
Figure 25 Global MLaaS : Software Tools and Services Adoption Trend 2016 vs 2021
Figure 26 Cloud and Web-Based Application Programming Interfaces Segment is Expected to Be the Fastest Growing in the Machine Learning as a Service Market
Figure 27 Managed Services Segment is Expected to Grow at the Highest CAGR During the Forecast Period
Figure 28 Network Analytics and Automated Traffic Management is Expected to Witness the Highest Adoption in the Forecast Period
Figure 29 Small and Medium Enterprises Segment is Projected to Grow at the Highest CAGR During the Forecast Period
Figure 30 Healthcare Vertical is Expected to Grow at the Highest CAGR During the Forecast Period
Figure 31 North America is Projected to Exhibit the Highest Growth Rate in the Machine Learning as a Service Market, 2016 - 2021
Figure 32 North America and Europe are the Most Attractive Markets for Investments, 2016-2021
Figure 33 North America Expected to Grow at the Highest CAGR, 2016-2021
Figure 34 North America Market Snapshot
Figure 35 Europe Market Snapshot
Figure 36 Asia-Pacific Market Snapshot
Figure 37 Companies Adopted Partnerships and Agreements as the Key Growth Strategy From 2013–2016
Figure 38 Market Evaluation Framework
Figure 39 Battle for Market Share: New Product Launches and Upgradations Was the Key Growth Strategy of the MLaaS Market
Figure 40 Geographic Revenue Mix of Top Players
Figure 41 Microsoft: Company Snapshot
Figure 42 Microsoft: SWOT Analysis
Figure 43 International Business Machine Corporation: Company Snapshot
Figure 44 International Business Machine Corporation: SWOT Analysis
Figure 45 Amazon Web Services: Company Snapshot
Figure 46 Amazon Web Services: SWOT Analysis
Figure 47 Google, Inc.: Company Snapshot
Figure 48 Google, Inc.: SWOT Analysis
Figure 49 BigML Inc.: SWOT Analysis
Figure 50 FICO: Company Snapshot
Figure 51 Hewlett Packard Enterprise Development LP: Company Snapshot


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Report Code
TC 4696
Published ON
Nov, 2016
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