Machine Learning as a Service (MLaaS) Market

[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

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)

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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.

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.

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