Machine Learning As A Service Mlaas Market
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
|
Years Covered |
2020-2028 |
|
Estimated Year Value (2021) |
US $2.42 BN |
|
Projected Year Value (2028) |
US $23.04 BN |
|
CAGR (2021 - 2028) |
38.0% |
|
Regions Covered |
North America, Europe, Asia Pacific, South America, and Middle East & Africa |
|
Countries Covered |
US, Canada, Mexico, Germany, UK, Italy, France, Spain, Japan, China, India, Australia, New Zealand, South Korea, Rest of Asia Pacific, South America, Argentina, Brazil, Chile, Middle East & Africa, Saudi Arabia, UAE, Qatar, and South Africa |
|
Largest Market |
North America |
|
Highest Growing Market |
Asia Pacific |
According to Stratistics MRC, the Global Machine Learning as a Service (MLaaS) Market is accounted for $2.42 billion in 2021 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.
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.
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
o US
o Canada
o Mexico
• Europe
o Germany
o UK
o Italy
o France
o Spain
o Rest of Europe
• Asia Pacific
o Japan
o China
o India
o Australia
o New Zealand
o South Korea
o Rest of Asia Pacific
• South America
o Argentina
o Brazil
o Chile
o Rest of South America
• Middle East & Africa
o Saudi Arabia
o UAE
o Qatar
o South Africa
o 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
o Comprehensive profiling of additional market players (up to 3)
o SWOT Analysis of key players (up to 3)
• Regional Segmentation
o Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
o 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
List of Tables
1 Global Machine Learning as a Service (MLaaS) Market Outlook, By Region (2019-2028) ($MN)
2 Global Machine Learning as a Service (MLaaS) Market Outlook, By Component (2019-2028) ($MN)
3 Global Machine Learning as a Service (MLaaS) Market Outlook, By Software Tools (2019-2028) ($MN)
4 Global Machine Learning as a Service (MLaaS) Market Outlook, By Data Storage and Archiving (2019-2028) ($MN)
5 Global Machine Learning as a Service (MLaaS) Market Outlook, By Modeler and Processing (2019-2028) ($MN)
6 Global Machine Learning as a Service (MLaaS) Market Outlook, By Services (2019-2028) ($MN)
7 Global Machine Learning as a Service (MLaaS) Market Outlook, By Professional Services (2019-2028) ($MN)
8 Global Machine Learning as a Service (MLaaS) Market Outlook, By Managed Services (2019-2028) ($MN)
9 Global Machine Learning as a Service (MLaaS) Market Outlook, By Deployment (2019-2028) ($MN)
10 Global Machine Learning as a Service (MLaaS) Market Outlook, By Cloud (2019-2028) ($MN)
11 Global Machine Learning as a Service (MLaaS) Market Outlook, By Public Cloud (2019-2028) ($MN)
12 Global Machine Learning as a Service (MLaaS) Market Outlook, By Private Cloud (2019-2028) ($MN)
13 Global Machine Learning as a Service (MLaaS) Market Outlook, By On Premise (2019-2028) ($MN)
14 Global Machine Learning as a Service (MLaaS) Market Outlook, By Organization Size (2019-2028) ($MN)
15 Global Machine Learning as a Service (MLaaS) Market Outlook, By Small and Medium Enterprises (2019-2028) ($MN)
16 Global Machine Learning as a Service (MLaaS) Market Outlook, By Large Enterprises (2019-2028) ($MN)
17 Global Machine Learning as a Service (MLaaS) Market Outlook, By Application (2019-2028) ($MN)
18 Global Machine Learning as a Service (MLaaS) Market Outlook, By Augmented & Virtual Reality (2019-2028) ($MN)
19 Global Machine Learning as a Service (MLaaS) Market Outlook, By Automated Network Management (2019-2028) ($MN)
20 Global Machine Learning as a Service (MLaaS) Market Outlook, By Computer Vision (2019-2028) ($MN)
21 Global Machine Learning as a Service (MLaaS) Market Outlook, By Fraud Detection and Risk Analytics (2019-2028) ($MN)
22 Global Machine Learning as a Service (MLaaS) Market Outlook, By Marketing and Advertisement (2019-2028) ($MN)
23 Global Machine Learning as a Service (MLaaS) Market Outlook, By Natural Language Processing (2019-2028) ($MN)
24 Global Machine Learning as a Service (MLaaS) Market Outlook, By Network Analytics and Automated Traffic Management (2019-2028) ($MN)
25 Global Machine Learning as a Service (MLaaS) Market Outlook, By Predictive Maintenance (2019-2028) ($MN)
26 Global Machine Learning as a Service (MLaaS) Market Outlook, By Security & Surveillance (2019-2028) ($MN)
27 Global Machine Learning as a Service (MLaaS) Market Outlook, By End User (2019-2028) ($MN)
28 Global Machine Learning as a Service (MLaaS) Market Outlook, By Aerospace and Defense (2019-2028) ($MN)
29 Global Machine Learning as a Service (MLaaS) Market Outlook, By Automation and Transportation (2019-2028) ($MN)
30 Global Machine Learning as a Service (MLaaS) Market Outlook, By Automotive (2019-2028) ($MN)
31 Global Machine Learning as a Service (MLaaS) Market Outlook, By Banking, Financial Services and Insurance (BFSI) (2019-2028) ($MN)
32 Global Machine Learning as a Service (MLaaS) Market Outlook, By E-Commerce (2019-2028) ($MN)
33 Global Machine Learning as a Service (MLaaS) Market Outlook, By Education (2019-2028) ($MN)
34 Global Machine Learning as a Service (MLaaS) Market Outlook, By Energy & Utilities (2019-2028) ($MN)
35 Global Machine Learning as a Service (MLaaS) Market Outlook, By Feedstock & Utilities (2019-2028) ($MN)
36 Global Machine Learning as a Service (MLaaS) Market Outlook, By Government (2019-2028) ($MN)
37 Global Machine Learning as a Service (MLaaS) Market Outlook, By Healthcare & Life Sciences (2019-2028) ($MN)
38 Global Machine Learning as a Service (MLaaS) Market Outlook, By IT and Telecom (2019-2028) ($MN)
39 Global Machine Learning as a Service (MLaaS) Market Outlook, By Manufacturing (2019-2028) ($MN)
40 Global Machine Learning as a Service (MLaaS) Market Outlook, By Media and Entertainment (2019-2028) ($MN)
41 Global Machine Learning as a Service (MLaaS) Market Outlook, By Retail (2019-2028) ($MN)
42 Global Machine Learning as a Service (MLaaS) Market Outlook, By Travel & Hospitality (2019-2028) ($MN)
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.
List of Figures
RESEARCH METHODOLOGY
We at ‘Stratistics’ opt for an extensive research approach which involves data mining, data validation, and data analysis. The various research sources include in-house repository, secondary research, competitor’s sources, social media research, client internal data, and primary research.
Our team of analysts prefers the most reliable and authenticated data sources in order to perform the comprehensive literature search. With access to most of the authenticated data bases our team highly considers the best mix of information through various sources to obtain extensive and accurate analysis.
Each report takes an average time of a month and a team of 4 industry analysts. The time may vary depending on the scope and data availability of the desired market report. The various parameters used in the market assessment are standardized in order to enhance the data accuracy.
Data Mining
The data is collected from several authenticated, reliable, paid and unpaid sources and is filtered depending on the scope & objective of the research. Our reports repository acts as an added advantage in this procedure. Data gathering from the raw material suppliers, distributors and the manufacturers is performed on a regular basis, this helps in the comprehensive understanding of the products value chain. Apart from the above mentioned sources the data is also collected from the industry consultants to ensure the objective of the study is in the right direction.
Market trends such as technological advancements, regulatory affairs, market dynamics (Drivers, Restraints, Opportunities and Challenges) are obtained from scientific journals, market related national & international associations and organizations.
Data Analysis
From the data that is collected depending on the scope & objective of the research the data is subjected for the analysis. The critical steps that we follow for the data analysis include:
- Product Lifecycle Analysis
- Competitor analysis
- Risk analysis
- Porters Analysis
- PESTEL Analysis
- SWOT Analysis
The data engineering is performed by the core industry experts considering both the Marketing Mix Modeling and the Demand Forecasting. The marketing mix modeling makes use of multiple-regression techniques to predict the optimal mix of marketing variables. Regression factor is based on a number of variables and how they relate to an outcome such as sales or profits.
Data Validation
The data validation is performed by the exhaustive primary research from the expert interviews. This includes telephonic interviews, focus groups, face to face interviews, and questionnaires to validate our research from all aspects. The industry experts we approach come from the leading firms, involved in the supply chain ranging from the suppliers, distributors to the manufacturers and consumers so as to ensure an unbiased analysis.
We are in touch with more than 15,000 industry experts with the right mix of consultants, CEO's, presidents, vice presidents, managers, experts from both supply side and demand side, executives and so on.
The data validation involves the primary research from the industry experts belonging to:
- Leading Companies
- Suppliers & Distributors
- Manufacturers
- Consumers
- Industry/Strategic Consultants
Apart from the data validation the primary research also helps in performing the fill gap research, i.e. providing solutions for the unmet needs of the research which helps in enhancing the reports quality.
For more details about research methodology, kindly write to us at info@strategymrc.com
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