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Global Machine Learning as a Service Market Size, Share & Industry Trends Analysis Report By End User, By Offering, By Organization Size, By Application, By Regional Outlook and Forecast, 2022 - 2028

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    Report

  • 337 Pages
  • May 2022
  • Region: Global
  • Marqual IT Solutions Pvt. Ltd (KBV Research)
  • ID: 5615650
The Global Machine learning as a Service Market size is expected to reach $36.2 billion by 2028, rising at a market growth of 31.6% CAGR during the forecast period.



Machine learning is a data analysis method that includes statistical data analysis to create desired prediction output without the use of explicit programming. It uses a sequence of algorithms to comprehend the link between datasets in order to produce the desired result. It is designed to include artificial intelligence (AI) and cognitive computing functionalities. Machine learning as a service (MLaaS) refers to a group of cloud computing services that provide machine learning technologies.

Increased demand for cloud computing, as well as growth connected with artificial intelligence and cognitive computing, are major machine learning as service industry growth drivers. Growth in demand for cloud-based solutions, such as cloud computing, rise in adoption of analytical solutions, growth of the artificial intelligence & cognitive computing market, increased application areas, and a scarcity of trained professionals are all influencing the machine learning as a service market.

As more businesses migrate their data from on-premise storage to cloud storage, the necessity for efficient data organization grows. Since MLaaS platforms are essentially cloud providers, they enable solutions to appropriately manage data for machine learning experiments and data pipelines, making it easier for data engineers to access and process the data.

For organizations, MLaaS providers offer capabilities like data visualization and predictive analytics. They also provide APIs for sentiment analysis, facial recognition, creditworthiness evaluations, corporate intelligence, and healthcare, among other things. The actual computations of these processes are abstracted by MLaaS providers, so data scientists don't have to worry about them. For machine learning experimentation and model construction, some MLaaS providers even feature a drag-and-drop interface.

COVID-19 Impact Analysis


The COVID-19 pandemic has had a substantial impact on numerous countries' health, economic, and social systems. It has resulted in millions of fatalities across the globe and has left the economic and financial systems in tatters. Individuals can benefit from knowledge about individual-level susceptibility variables in order to better understand and cope with their psychological, emotional, and social well-being.

Artificial intelligence technology is likely to aid in the fight against the COVID-19 pandemic. COVID-19 cases are being tracked and traced in several countries utilizing population monitoring approaches enabled by machine learning and artificial intelligence. Researchers in South Korea, for example, track coronavirus cases using surveillance camera footage and geo-location data.

Market Growth Factors


Increased Demand for Cloud Computing and a Boom in Big Data


The industry is growing due to the increased acceptance of cloud computing technologies and the use of social media platforms. Cloud computing is now widely used by all companies that supply enterprise storage solutions. Data analysis is performed online using cloud storage, giving the advantage of evaluating real-time data collected on the cloud. Cloud computing enables data analysis from any location and at any time. Moreover, using the cloud to deploy machine learning allows businesses to get useful data, such as consumer behavior and purchasing trends, virtually from linked data warehouses, lowering infrastructure and storage costs. As a result, the machine learning as a service business is growing as cloud computing technology becomes more widely adopted.

Use of Machine Learning to Fuel Artificial Intelligence Systems


Machine learning is used to fuel reasoning, learning, and self-correction in artificial intelligence (AI) systems. Expert systems, speech recognition, and machine vision are examples of AI applications. The rise in the popularity of AI is due to current efforts such as big data infrastructure and cloud computing. Top companies across industries, including Google, Microsoft, and Amazon (Software & IT); Bloomberg, American Express (Financial Services); and Tesla and Ford (Automotive), have identified AI and cognitive computing as a key strategic driver and have begun investing in machine learning to develop more advanced systems. These top firms have also provided financial support to young start-ups in order to produce new creative technology.

Market Restraining Factors


Technical Restraints and Inaccuracies of ML


The ML platform provides a plethora of advantages that aid in market expansion. However, several parameters on the platform are projected to impede market expansion. The presence of inaccuracy in these algorithms, which are sometimes immature and underdeveloped, is one of the market's primary constraining factors. In the big data and machine learning manufacturing industries, precision is crucial. A minor flaw in the algorithm could result in incorrect items being produced. This is expected to exorbitantly increase the operational costs for the owner of the manufacturing unit than decrease it.

End User Outlook


Based on End User, the market is segmented into IT & Telecom, BFSI, Manufacturing, Retail, Healthcare, Energy & Utilities, Public Sector, Aerospace & Defense, and Others. The retail segment garnered a substantial revenue share in the machine learning as a service market in 2021. E-commerce has proven to be a key force in the retail trade industry. Machine intelligence is used by retailers to collect data, evaluate it, and use it to provide customers with individualized shopping experiences. These are some of the factors that influence the retail industries' demand for this technology.

Offering Outlook


Based on Offering, the market is segmented into Services Only and Solution (Software Tools). The services only segment acquired the largest revenue share in the machine learning as a service market in 2021. The market for machine learning services is expected to grow due to factors such as an increase in application areas and growth connected with end-use industries in developing economies. To enhance the usage of machine learning services, industry participants are focusing on implementing technologically advanced solutions. The use of machine learning services in the healthcare business for cancer detection, as well as checking ECG and MRI, is expanding the market. Machine learning services' benefits, such as cost reduction, demand forecasting, real-time data analysis, and increased cloud use, are projected to open up considerable prospects for the market.

Organization Size Outlook


Based on Organization Size, the market is segmented into Large Enterprises and Small & Medium Enterprises. The small and medium enterprises segment procured a substantial revenue share in the machine learning as a service market in 2021. This is because implementation of machine learning lets SMEs optimize its processes on a tight budget. AI and machine learning are projected to be the major technologies that allow SMEs to save money on ICT and gain access to digital resources in the near future.

Application Outlook


Based on Application, the market is segmented into Marketing & Advertising, Fraud Detection & Risk Management, Computer vision, Security & Surveillance, Predictive analytics, Natural Language Processing, Augmented & Virtual Reality, and Others. The marketing and advertising segment acquired the largest revenue share in the machine learning as a service market in 2021. The goal of a recommendation system is to provide customers with products that they are currently interested in. The following is the marketing work algorithm: Hypotheses are developed, tested, evaluated, and analyzed by marketers. Because information changes every second, this effort is time-consuming and labor-intensive, and the findings are occasionally wrong. Machine learning allows marketers to make quick decisions based on large amounts of data. Machine learning allows businesses to respond more quickly to changes in the quality of traffic generated by advertising efforts. As a result, the business can spend more time developing hypotheses rather than doing mundane tasks.

Regional Outlook


Based on Regions, the market is segmented into North America, Europe, Asia Pacific, and Latin America, Middle East & Africa. The Asia Pacific region garnered a significant revenue share in the machine learning as a service market in 2021. Leading companies are concentrating their efforts in Asia-Pacific to expand their operations, as the region is likely to see rapid development in the deployment of security services, particularly in the banking, financial services, and insurance (BFSI) sector. To provide better customer service, industry participants are realizing the significance of providing multi-modal platforms. The rise in AI application adoption is likely to be the primary trend driving market growth in this area. Furthermore, government organizations have taken important steps to accelerate the adoption of machine learning and related technologies in this region.

The major strategies followed by the market participants are Product Launches and Partnerships. Based on the Analysis presented in the Cardinal matrix; Amazon Web Services, Inc., Microsoft Corporation, and Google LLC are the forerunners in the Machine learning as a Service Market. Companies such SAS Institute, Inc., IBM Corporation are some of the key innovators in the Market.

The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Hewlett-Packard Enterprise Company, Oracle Corporation, Google LLC, Amazon Web Services, Inc. (Amazon.com, Inc.), IBM Corporation, Microsoft Corporation, Fair Isaac Corporation (FICO), SAS Institute, Inc., Yottamine Analytics, LLC, and BigML.

» Partnerships, Collaborations and Agreements:

  • Mar-2022: Google entered into a partnership with BT, a British telecommunications company. Under the partnership, BT utilized a suite of Google Cloud products and services - including cloud infrastructure, machine learning (ML) and artificial intelligence (AI), data analytics, security, and API management - to offer excellent customer experiences, decrease costs, and risks, and create more revenue streams. Google aimed to enable BT to get access to hundreds of new business use-cases to solidify its goals around digital offerings and developing hyper-personalized customer engagement.
  • Feb-2022: SAS entered into a partnership with TecCentric, a company providing customized IT solutions. SAS aimed to fasten TecCentric's journey towards discovery with artificial intelligence (AI), machine learning (ML), and advanced analytics. Under the partnership, TecCentric aimed to work with SAS to customize services and solutions for a broad range of verticals from the public sector, to banking, education, healthcare, and more, granting them access to the complete analytics cycle with SAS's enhanced AI solution offering as well as its leading fraud and financial crimes analytics and reporting.
  • Feb-2022: Microsoft entered into a partnership with Tata Consultancy Services, an Indian company focusing on providing information technology services and consulting. Under the partnership, Tata Consultancy Services leveraged its software, TCS Intelligent Urban Exchange (IUX) and TCS Customer Intelligence & Insights (CI&I), to enable businesses in providing hyper-personalized customer experiences. CI&I and IUX are supported by artificial intelligence (AI), and machine learning, and assist in real-time data analytics. The CI&I software empowered retailers, banks, insurers, and other businesses to gather insights, predictions, and recommended actions in real-time to enhance the satisfaction of customers.
  • Jun-2021: Amazon Web Services entered into a partnership with Salesforce, a cloud-based software company. The partnership enabled to utilize complete set of Salesforce and AWS capabilities simultaneously to rapidly develop and deploy new business applications that facilitate digital transformation. Salesforce also embedded AWS services for voice, video, artificial intelligence (AI), and machine learning (ML) directly in new applications for sales, service, and industry vertical use cases.
  • Apr-2021: Amazon formed a partnership with Basler, a company known for its product line of area scan, line scan, and network cameras. The partnership began as Amazon launched a succession of services for industrial machine learning, including its latest Lookout for Vision cloud AI service for factory inspection. Customers can integrate AWS Panorama SDK within its platform, and thus utilize a common architecture to perform multiple tasks and accommodate a broad range of performance and cost. The integration of AWS Panorama empowered customers to adopt and run machine learning applications on edge devices with additional support for device management and accuracy tracking.  
  • Dec-2020: IBM teamed up with Mila, a Quebec Artificial Intelligence Institute. Under the collaboration, both organizations aimed to quicken machine learning using Oríon, an open-source technology. After the integration of Mila’s open-source Oríon software and IBM’s Watson Machine Learning Accelerator, IBM also enhanced the deployment of state-of-the-art algorithms, along with improved machine learning and deep learning capabilities for AI researchers and data scientists. IBM’s Spectrum Computing team based out of Canada Lab contributes substantially to Oríon’s code base.
  • Oct-2020: SAS entered into a partnership with TMA Solutions, a software outsourcing company based in Vietnam. Under the partnership, SAS and TMA Solutions aimed to fasten the growth of businesses in Vietnam through Artificial Intelligence (AI) and Data Analytics. SAS and TMA helped clients in Vietnam quicken the deployment and growth of advanced analytics and look for new methods to propel innovation in AI, especially in the fields of Machine Learning, Computer Vision, Natural Language Processing (NLP), and other technologies.

» Product Launches and Product Expansions:

  • May-2022: Hewlett Packard launched HPE Swarm Learning and the new Machine Learning (ML) Development System, two AI and ML-based solutions. These new solutions increase the accuracy of models, solve AI infrastructure burdens, and improve data privacy standards. The company declared the new tool a “breakthrough AI solution” that focuses on fast-tracking insights at the edge, with attributes ranging from identifying card fraud to diagnosing diseases.
  • Apr-2022: Hewlett Packard released Machine Learning Development System (MLDS) and Swarm Learning, its new machine learning solutions. The two solutions are focused on simplifying the burdens of AI development in a development environment that progressively consists of large amounts of protected data and specialized hardware. The MLDS provides a full software and services stack, including a training platform (the HPE Machine Learning Development Environment), container management (Docker), cluster management (HPE Cluster Manager), and Red Hat Enterprise Linux.
  • May-2021: Google released Vertex AI, a novel managed machine learning platform that enables developers to more easily deploy and maintain their AI models. Engineers can use Vertex AI to manage video, image, text, and tabular datasets, and develop machine learning pipelines to train and analyze models utilizing Google Cloud algorithms or custom training code. After that the engineers can install models for online or batch use cases all on scalable managed infrastructure.
  • Mar-2021: Microsoft released updates to Azure Arc, its service that brought Azure products and management to multiple clouds, edge devices, and data centers with auditing, compliance, and role-based access. Microsoft also made Azure Arc-enabled Kubernetes available. Azure Arc-enabled Machine Learning and Azure Arc-enabled Kubernetes are developed to aid companies to find a balance between enjoying the advantages of the cloud and maintaining apps and maintaining apps and workloads on-premises for regulatory and operational reasons. The new services enable companies to implement Kubernetes clusters and create machine learning models where data lives, as well as handle applications and models from a single dashboard.
  • Jul-2020: Hewlett Packard released HPE Ezmeral, a new brand and software portfolio developed to assist enterprises to quicken digital transformation across their organization, from edge to cloud. The HPE Ezmeral goes from a portfolio consisting of container orchestration and management, AI/ML, and data analytics to cost control, IT automation and AI-driven operations, and security.

» Acquisitions and Mergers:


Jun-2021: Hewlett Packard completed the acquisition of Determined AI, a San Francisco-based startup that offers a strong and solid software stack to train AI models faster, at any scale, utilizing its open-source machine learning (ML) platform. Hewlett Packard integrated Determined AI’s unique software solution with its world-leading AI and high-performance computing (HPC) products to empower ML  engineers to conveniently deploy and train machine learning models to offer faster and more precise analysis from their data in almost every industry.

Scope of the Study


Market Segments Covered in the Report:


By End User

  • IT & Telecom
  • BFSI
  • Manufacturing
  • Retail
  • Healthcare
  • Energy & Utilities
  • Public Sector
  • Aerospace & Defense
  • Others

By Offering

  • Services Only
  • Solution (Software Tools)

By Organization Size

  • Large Enterprises
  • Small & Medium Enterprises

By Application

  • Marketing & Advertising
  • Fraud Detection & Risk Management
  • Computer vision
  • Security & Surveillance
  • Predictive analytics
  • Natural Language Processing
  • Augmented & Virtual Reality
  • Others

By Geography

  • North America
  • US
  • Canada
  • Mexico
  • Rest of North America
  • Europe
  • Germany
  • UK
  • France
  • Russia
  • Spain
  • Italy
  • Rest of Europe
  • Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • Singapore
  • Malaysia
  • Rest of Asia Pacific
  • LAMEA
  • Brazil
  • Argentina
  • UAE
  • Saudi Arabia
  • South Africa
  • Nigeria
  • Rest of LAMEA

Key Market Players


List of Companies Profiled in the Report:

  • Hewlett-Packard Enterprise Company
  • Oracle Corporation
  • Google LLC
  • Amazon Web Services, Inc. (Amazon.com, Inc.)
  • IBM Corporation
  • Microsoft Corporation
  • Fair Isaac Corporation (FICO)
  • SAS Institute, Inc.
  • Yottamine Analytics, LLC
  • BigML

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Frequently Asked Questions about the Global Machine learning as a Service Market

What is the estimated value of the Global Machine learning as a Service Market?

The Global Machine learning as a Service Market was estimated to be valued at $5515 Million in 2021.

What is the growth rate of the Global Machine learning as a Service Market?

The growth rate of the Global Machine learning as a Service Market is 31.6%, with an estimated value of $36204 Million by 2028.

What is the forecasted size of the Global Machine learning as a Service Market?

The Global Machine learning as a Service Market is estimated to be worth $36204 Million by 2028.

Who are the key companies in the Global Machine learning as a Service Market?

Key companies in the Global Machine learning as a Service Market include Hewlett, Packard Enterprise Company, Oracle Corporation, Google LLC, Amazon Web Services, Inc. (Amazon.com, Inc.), IBM Corporation, Microsoft Corporation, Fair Isaac Corporation (FICO) and Yottamine Analytics, LLC.

Table of Contents

Chapter 1. Market Scope & Methodology
1.1 Market Definition
1.2 Objectives
1.3 Market Scope
1.4 Segmentation
1.4.1 Global Machine learning as a Service Market, by End User
1.4.2 Global Machine learning as a Service Market, by Offering
1.4.3 Global Machine learning as a Service Market, by Organization Size
1.4.4 Global Machine learning as a Service Market, by Application
1.4.5 Global Machine learning as a Service Market, by Geography
1.5 Methodology for the research
Chapter 2. Market Overview
2.1 Introduction
2.1.1 Overview
2.1.1.1 Market Composition and Scenario
2.2 Key Factors Impacting the Market
2.2.1 Market Drivers
2.2.2 Market Restraints
Chapter 3. Competition Analysis - Global
3.1 KBV Cardinal Matrix
3.2 Recent Industry Wide Strategic Developments
3.2.1 Partnerships, Collaborations and Agreements
3.2.2 Product Launches and Product Expansions
3.2.3 Acquisition and Mergers
3.3 Market Share Analysis, 2021
3.4 Top Winning Strategies
3.4.1 Key Leading Strategies: Percentage Distribution (2018-2022)
3.4.2 Key Strategic Move: (Product Launches and Product Expansions : 2018, Jan - 2022, May) Leading Players
3.4.3 Key Strategic Move: (Partnership, Collaboration and Agreement : 2019, Apr - 2022, Mar) Leading Players
Chapter 4. Global Machine learning as a Service Market by End User
4.1 Global IT & Telecom Market by Region
4.2 Global BFSI Market by Region
4.3 Global Manufacturing Market by Region
4.4 Global Retail Market by Region
4.5 Global Healthcare Market by Region
4.6 Global Energy & Utilities Market by Region
4.7 Global Public Sector Market by Region
4.8 Global Aerospace & Defense Market by Region
4.9 Global Other End User Market by Region
Chapter 5. Global Machine learning as a Service Market by Offering
5.1 Global Services Only Market by Region
5.2 Global Solution (Software Tools) Market by Region
Chapter 6. Global Machine learning as a Service Market by Organization Size
6.1 Global Large Enterprises Market by Region
6.2 Global Small & Medium Enterprises Market by Region
Chapter 7. Global Machine learning as a Service Market by Application
7.1 Global Marketing & Advertising Market by Region
7.2 Global Fraud Detection & Risk Management Market by Region
7.3 Global Computer vision Market by Region
7.4 Global Security & Surveillance Market by Region
7.5 Global Predictive analytics Market by Region
7.6 Global Natural Language Processing Market by Region
7.7 Global Augmented & Virtual Reality Market by Region
7.8 Global Others Market by Region
Chapter 8. Global Machine learning as a Service Market by Region
8.1 North America Machine learning as a Service Market
8.1.1 North America Machine learning as a Service Market by End User
8.1.1.1 North America IT & Telecom Market by Country
8.1.1.2 North America BFSI Market by Country
8.1.1.3 North America Manufacturing Market by Country
8.1.1.4 North America Retail Market by Country
8.1.1.5 North America Healthcare Market by Country
8.1.1.6 North America Energy & Utilities Market by Country
8.1.1.7 North America Public Sector Market by Country
8.1.1.8 North America Aerospace & Defense Market by Country
8.1.1.9 North America Other End User Market by Country
8.1.2 North America Machine learning as a Service Market by Offering
8.1.2.1 North America Services Only Market by Country
8.1.2.2 North America Solution (Software Tools) Market by Country
8.1.3 North America Machine learning as a Service Market by Organization Size
8.1.3.1 North America Large Enterprises Market by Country
8.1.3.2 North America Small & Medium Enterprises Market by Country
8.1.4 North America Machine learning as a Service Market by Application
8.1.4.1 North America Marketing & Advertising Market by Country
8.1.4.2 North America Fraud Detection & Risk Management Market by Country
8.1.4.3 North America Computer vision Market by Country
8.1.4.4 North America Security & Surveillance Market by Country
8.1.4.5 North America Predictive analytics Market by Country
8.1.4.6 North America Natural Language Processing Market by Country
8.1.4.7 North America Augmented & Virtual Reality Market by Country
8.1.4.8 North America Others Market by Country
8.1.5 North America Machine learning as a Service Market by Country
8.1.5.1 US Machine learning as a Service Market
8.1.5.1.1 US Machine learning as a Service Market by End User
8.1.5.1.2 US Machine learning as a Service Market by Offering
8.1.5.1.3 US Machine learning as a Service Market by Organization Size
8.1.5.1.4 US Machine learning as a Service Market by Application
8.1.5.2 Canada Machine learning as a Service Market
8.1.5.2.1 Canada Machine learning as a Service Market by End User
8.1.5.2.2 Canada Machine learning as a Service Market by Offering
8.1.5.2.3 Canada Machine learning as a Service Market by Organization Size
8.1.5.2.4 Canada Machine learning as a Service Market by Application
8.1.5.3 Mexico Machine learning as a Service Market
8.1.5.3.1 Mexico Machine learning as a Service Market by End User
8.1.5.3.2 Mexico Machine learning as a Service Market by Offering
8.1.5.3.3 Mexico Machine learning as a Service Market by Organization Size
8.1.5.3.4 Mexico Machine learning as a Service Market by Application
8.1.5.4 Rest of North America Machine learning as a Service Market
8.1.5.4.1 Rest of North America Machine learning as a Service Market by End User
8.1.5.4.2 Rest of North America Machine learning as a Service Market by Offering
8.1.5.4.3 Rest of North America Machine learning as a Service Market by Organization Size
8.1.5.4.4 Rest of North America Machine learning as a Service Market by Application
8.2 Europe Machine learning as a Service Market
8.2.1 Europe Machine learning as a Service Market by End User
8.2.1.1 Europe IT & Telecom Market by Country
8.2.1.2 Europe BFSI Market by Country
8.2.1.3 Europe Manufacturing Market by Country
8.2.1.4 Europe Retail Market by Country
8.2.1.5 Europe Healthcare Market by Country
8.2.1.6 Europe Energy & Utilities Market by Country
8.2.1.7 Europe Public Sector Market by Country
8.2.1.8 Europe Aerospace & Defense Market by Country
8.2.1.9 Europe Other End User Market by Country
8.2.2 Europe Machine learning as a Service Market by Offering
8.2.2.1 Europe Services Only Market by Country
8.2.2.2 Europe Solution (Software Tools) Market by Country
8.2.3 Europe Machine learning as a Service Market by Organization Size
8.2.3.1 Europe Large Enterprises Market by Country
8.2.3.2 Europe Small & Medium Enterprises Market by Country
8.2.4 Europe Machine learning as a Service Market by Application
8.2.4.1 Europe Marketing & Advertising Market by Country
8.2.4.2 Europe Fraud Detection & Risk Management Market by Country
8.2.4.3 Europe Computer vision Market by Country
8.2.4.4 Europe Security & Surveillance Market by Country
8.2.4.5 Europe Predictive analytics Market by Country
8.2.4.6 Europe Natural Language Processing Market by Country
8.2.4.7 Europe Augmented & Virtual Reality Market by Country
8.2.4.8 Europe Others Market by Country
8.2.5 Europe Machine learning as a Service Market by Country
8.2.5.1 Germany Machine learning as a Service Market
8.2.5.1.1 Germany Machine learning as a Service Market by End User
8.2.5.1.2 Germany Machine learning as a Service Market by Offering
8.2.5.1.3 Germany Machine learning as a Service Market by Organization Size
8.2.5.1.4 Germany Machine learning as a Service Market by Application
8.2.5.2 UK Machine learning as a Service Market
8.2.5.2.1 UK Machine learning as a Service Market by End User
8.2.5.2.2 UK Machine learning as a Service Market by Offering
8.2.5.2.3 UK Machine learning as a Service Market by Organization Size
8.2.5.2.4 UK Machine learning as a Service Market by Application
8.2.5.3 France Machine learning as a Service Market
8.2.5.3.1 France Machine learning as a Service Market by End User
8.2.5.3.2 France Machine learning as a Service Market by Offering
8.2.5.3.3 France Machine learning as a Service Market by Organization Size
8.2.5.3.4 France Machine learning as a Service Market by Application
8.2.5.4 Russia Machine learning as a Service Market
8.2.5.4.1 Russia Machine learning as a Service Market by End User
8.2.5.4.2 Russia Machine learning as a Service Market by Offering
8.2.5.4.3 Russia Machine learning as a Service Market by Organization Size
8.2.5.4.4 Russia Machine learning as a Service Market by Application
8.2.5.5 Spain Machine learning as a Service Market
8.2.5.5.1 Spain Machine learning as a Service Market by End User
8.2.5.5.2 Spain Machine learning as a Service Market by Offering
8.2.5.5.3 Spain Machine learning as a Service Market by Organization Size
8.2.5.5.4 Spain Machine learning as a Service Market by Application
8.2.5.6 Italy Machine learning as a Service Market
8.2.5.6.1 Italy Machine learning as a Service Market by End User
8.2.5.6.2 Italy Machine learning as a Service Market by Offering
8.2.5.6.3 Italy Machine learning as a Service Market by Organization Size
8.2.5.6.4 Italy Machine learning as a Service Market by Application
8.2.5.7 Rest of Europe Machine learning as a Service Market
8.2.5.7.1 Rest of Europe Machine learning as a Service Market by End User
8.2.5.7.2 Rest of Europe Machine learning as a Service Market by Offering
8.2.5.7.3 Rest of Europe Machine learning as a Service Market by Organization Size
8.2.5.7.4 Rest of Europe Machine learning as a Service Market by Application
8.3 Asia Pacific Machine learning as a Service Market
8.3.1 Asia Pacific Machine learning as a Service Market by End User
8.3.1.1 Asia Pacific IT & Telecom Market by Country
8.3.1.2 Asia Pacific BFSI Market by Country
8.3.1.3 Asia Pacific Manufacturing Market by Country
8.3.1.4 Asia Pacific Retail Market by Country
8.3.1.5 Asia Pacific Healthcare Market by Country
8.3.1.6 Asia Pacific Energy & Utilities Market by Country
8.3.1.7 Asia Pacific Public Sector Market by Country
8.3.1.8 Asia Pacific Aerospace & Defense Market by Country
8.3.1.9 Asia Pacific Other End User Market by Country
8.3.2 Asia Pacific Machine learning as a Service Market by Offering
8.3.2.1 Asia Pacific Services Only Market by Country
8.3.2.2 Asia Pacific Solution (Software Tools) Market by Country
8.3.3 Asia Pacific Machine learning as a Service Market by Organization Size
8.3.3.1 Asia Pacific Large Enterprises Market by Country
8.3.3.2 Asia Pacific Small & Medium Enterprises Market by Country
8.3.4 Asia Pacific Machine learning as a Service Market by Application
8.3.4.1 Asia Pacific Marketing & Advertising Market by Country
8.3.4.2 Asia Pacific Fraud Detection & Risk Management Market by Country
8.3.4.3 Asia Pacific Computer vision Market by Country
8.3.4.4 Asia Pacific Security & Surveillance Market by Country
8.3.4.5 Asia Pacific Predictive analytics Market by Country
8.3.4.6 Asia Pacific Natural Language Processing Market by Country
8.3.4.7 Asia Pacific Augmented & Virtual Reality Market by Country
8.3.4.8 Asia Pacific Others Market by Country
8.3.5 Asia Pacific Machine learning as a Service Market by Country
8.3.5.1 China Machine learning as a Service Market
8.3.5.1.1 China Machine learning as a Service Market by End User
8.3.5.1.2 China Machine learning as a Service Market by Offering
8.3.5.1.3 China Machine learning as a Service Market by Organization Size
8.3.5.1.4 China Machine learning as a Service Market by Application
8.3.5.2 Japan Machine learning as a Service Market
8.3.5.2.1 Japan Machine learning as a Service Market by End User
8.3.5.2.2 Japan Machine learning as a Service Market by Offering
8.3.5.2.3 Japan Machine learning as a Service Market by Organization Size
8.3.5.2.4 Japan Machine learning as a Service Market by Application
8.3.5.3 India Machine learning as a Service Market
8.3.5.3.1 India Machine learning as a Service Market by End User
8.3.5.3.2 India Machine learning as a Service Market by Offering
8.3.5.3.3 India Machine learning as a Service Market by Organization Size
8.3.5.3.4 India Machine learning as a Service Market by Application
8.3.5.4 South Korea Machine learning as a Service Market
8.3.5.4.1 South Korea Machine learning as a Service Market by End User
8.3.5.4.2 South Korea Machine learning as a Service Market by Offering
8.3.5.4.3 South Korea Machine learning as a Service Market by Organization Size
8.3.5.4.4 South Korea Machine learning as a Service Market by Application
8.3.5.5 Singapore Machine learning as a Service Market
8.3.5.5.1 Singapore Machine learning as a Service Market by End User
8.3.5.5.2 Singapore Machine learning as a Service Market by Offering
8.3.5.5.3 Singapore Machine learning as a Service Market by Organization Size
8.3.5.5.4 Singapore Machine learning as a Service Market by Application
8.3.5.6 Malaysia Machine learning as a Service Market
8.3.5.6.1 Malaysia Machine learning as a Service Market by End User
8.3.5.6.2 Malaysia Machine learning as a Service Market by Offering
8.3.5.6.3 Malaysia Machine learning as a Service Market by Organization Size
8.3.5.6.4 Malaysia Machine learning as a Service Market by Application
8.3.5.7 Rest of Asia Pacific Machine learning as a Service Market
8.3.5.7.1 Rest of Asia Pacific Machine learning as a Service Market by End User
8.3.5.7.2 Rest of Asia Pacific Machine learning as a Service Market by Offering
8.3.5.7.3 Rest of Asia Pacific Machine learning as a Service Market by Organization Size
8.3.5.7.4 Rest of Asia Pacific Machine learning as a Service Market by Application
8.4 LAMEA Machine learning as a Service Market
8.4.1 LAMEA Machine learning as a Service Market by End User
8.4.1.1 LAMEA IT & Telecom Market by Country
8.4.1.2 LAMEA BFSI Market by Country
8.4.1.3 LAMEA Manufacturing Market by Country
8.4.1.4 LAMEA Retail Market by Country
8.4.1.5 LAMEA Healthcare Market by Country
8.4.1.6 LAMEA Energy & Utilities Market by Country
8.4.1.7 LAMEA Public Sector Market by Country
8.4.1.8 LAMEA Aerospace & Defense Market by Country
8.4.1.9 LAMEA Other End User Market by Country
8.4.2 LAMEA Machine learning as a Service Market by Offering
8.4.2.1 LAMEA Services Only Market by Country
8.4.2.2 LAMEA Solution (Software Tools) Market by Country
8.4.3 LAMEA Machine learning as a Service Market by Organization Size
8.4.3.1 LAMEA Large Enterprises Market by Country
8.4.3.2 LAMEA Small & Medium Enterprises Market by Country
8.4.4 LAMEA Machine learning as a Service Market by Application
8.4.4.1 LAMEA Marketing & Advertising Market by Country
8.4.4.2 LAMEA Fraud Detection & Risk Management Market by Country
8.4.4.3 LAMEA Computer vision Market by Country
8.4.4.4 LAMEA Security & Surveillance Market by Country
8.4.4.5 LAMEA Predictive analytics Market by Country
8.4.4.6 LAMEA Natural Language Processing Market by Country
8.4.4.7 LAMEA Augmented & Virtual Reality Market by Country
8.4.4.8 LAMEA Others Market by Country
8.4.5 LAMEA Machine learning as a Service Market by Country
8.4.5.1 Brazil Machine learning as a Service Market
8.4.5.1.1 Brazil Machine learning as a Service Market by End User
8.4.5.1.2 Brazil Machine learning as a Service Market by Offering
8.4.5.1.3 Brazil Machine learning as a Service Market by Organization Size
8.4.5.1.4 Brazil Machine learning as a Service Market by Application
8.4.5.2 Argentina Machine learning as a Service Market
8.4.5.2.1 Argentina Machine learning as a Service Market by End User
8.4.5.2.2 Argentina Machine learning as a Service Market by Offering
8.4.5.2.3 Argentina Machine learning as a Service Market by Organization Size
8.4.5.2.4 Argentina Machine learning as a Service Market by Application
8.4.5.3 UAE Machine learning as a Service Market
8.4.5.3.1 UAE Machine learning as a Service Market by End User
8.4.5.3.2 UAE Machine learning as a Service Market by Offering
8.4.5.3.3 UAE Machine learning as a Service Market by Organization Size
8.4.5.3.4 UAE Machine learning as a Service Market by Application
8.4.5.4 Saudi Arabia Machine learning as a Service Market
8.4.5.4.1 Saudi Arabia Machine learning as a Service Market by End User
8.4.5.4.2 Saudi Arabia Machine learning as a Service Market by Offering
8.4.5.4.3 Saudi Arabia Machine learning as a Service Market by Organization Size
8.4.5.4.4 Saudi Arabia Machine learning as a Service Market by Application
8.4.5.5 South Africa Machine learning as a Service Market
8.4.5.5.1 South Africa Machine learning as a Service Market by End User
8.4.5.5.2 South Africa Machine learning as a Service Market by Offering
8.4.5.5.3 South Africa Machine learning as a Service Market by Organization Size
8.4.5.5.4 South Africa Machine learning as a Service Market by Application
8.4.5.6 Nigeria Machine learning as a Service Market
8.4.5.6.1 Nigeria Machine learning as a Service Market by End User
8.4.5.6.2 Nigeria Machine learning as a Service Market by Offering
8.4.5.6.3 Nigeria Machine learning as a Service Market by Organization Size
8.4.5.6.4 Nigeria Machine learning as a Service Market by Application
8.4.5.7 Rest of LAMEA Machine learning as a Service Market
8.4.5.7.1 Rest of LAMEA Machine learning as a Service Market by End User
8.4.5.7.2 Rest of LAMEA Machine learning as a Service Market by Offering
8.4.5.7.3 Rest of LAMEA Machine learning as a Service Market by Organization Size
8.4.5.7.4 Rest of LAMEA Machine learning as a Service Market by Application
Chapter 9. Company Profiles
9.1 Hewlett Packard Enterprise Company
9.1.1 Company Overview
9.1.2 Financial Analysis
9.1.3 Segmental and Regional Analysis
9.1.4 Research & Development Expense
9.1.5 Recent strategies and developments:
9.1.5.1 Product Launches and Product Expansions:
9.1.5.2 Acquisition and Mergers:
9.2 Oracle Corporation
9.2.1 Company Overview
9.2.2 Financial Analysis
9.2.3 Segmental and Regional Analysis
9.2.4 Research & Development Expense
9.2.5 SWOT Analysis
9.3 Google LLC
9.3.1 Company Overview
9.3.2 Financial Analysis
9.3.3 Segmental and Regional Analysis
9.3.4 Research & Development Expense
9.3.5 Recent strategies and developments:
9.3.5.1 Partnerships, Collaborations, and Agreements:
9.3.5.2 Product Launches and Product Expansions:
9.4 Amazon Web Services, Inc. (Amazon.com, Inc.)
9.4.1 Company Overview
9.4.2 Financial Analysis
9.4.3 Segmental Analysis
9.4.4 Recent strategies and developments:
9.4.4.1 Partnerships, Collaborations, and Agreements:
9.4.4.2 Product Launches and Product Expansions:
9.5 IBM Corporation
9.5.1 Company Overview
9.5.2 Financial Analysis
9.5.3 Regional & Segmental Analysis
9.5.4 Research & Development Expenses
9.5.5 Recent strategies and developments:
9.5.5.1 Partnerships, Collaborations, and Agreements:
9.6 Microsoft Corporation
9.6.1 Company Overview
9.6.2 Financial Analysis
9.6.3 Segmental and Regional Analysis
9.6.4 Research & Development Expenses
9.6.5 Recent strategies and developments:
9.6.5.1 Partnerships, Collaborations, and Agreements:
9.6.5.2 Product Launches and Product Expansions:
9.7 Fair Isaac Corporation (FICO)
9.7.1 Company Overview
9.7.2 Financial Analysis
9.7.3 Segmental and Regional Analysis
9.7.4 Research & Development Expenses
9.8 SAS Institute, Inc.
9.8.1 Company Overview
9.8.2 Recent strategies and developments:
9.8.2.1 Partnerships, Collaborations, and Agreements:
9.9 Yottamine Analytics, LLC
9.9.1 Company Overview
9.10. BigML
9.10.1 Company Overview

Companies Mentioned

  • Hewlett-Packard Enterprise Company
  • Oracle Corporation
  • Google LLC
  • Amazon Web Services, Inc. (Amazon.com, Inc.)
  • IBM Corporation
  • Microsoft Corporation
  • Fair Isaac Corporation (FICO)
  • SAS Institute, Inc.
  • Yottamine Analytics, LLC
  • BigML

Methodology

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