Global Machine Learning as a Service (MLaaS) Market 2019-2024: Projecting a CAGR of Over 43% -

DUBLIN--()--The "Machine Learning as a Service (MLaaS) Market - Growth, Trends and Forecast (2019 - 2024)" report has been added to's offering.

Machine Learning as a Service (MLaaS) Market is expected to witness at a CAGR of over 43% during the forecast period 2019-2024.

With advancements in data science and artificial intelligence, the performance of machine learning has accelerated at a rapid pace. 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.

Key Highlights

  • Artificial intelligence has emerged as an enabler for such solutions, and ML consequently plays a critical role.
  • MLaaS model is expected to dominate the market, with users having an option to choose from a wide variety of solutions that are focused on different business needs.
  • The increasing rate of adoption for IoT and automation systems in industries is expected to drive the growth of adoption rate for MLaaS. Industrial automation already has over a billion connected devices deployed. Owing to IoT, smart and connected technologies have increased the pace of creating vast amounts of data, which can be analyzed to gain profitable insights.

Major Market Trends

Retail Sector is Expected to Hold Major Market Share

  • The retail industry is booming in the digital space. The revolution is started by some of the prominent companies such as Amazon and eBay that have led to huge challenges for the traditional retail business model, but also have massive potential for retailers and consumers alike.
  • Retail and consumer goods companies are seeing the applicability of machine learning (ML) to drive improvements in customer service and operational efficiency. For instance, the Azure cloud is helping retail and consumer brands to improve the shopping experience by ensuring that shelves are stocked and the products are always available when, where and how the consumer wants to shop.

North America is Expected to Hold the Largest Market Share

  • North America is driven by the swift market penetration and presence of large companies, working on the technology which has influenced the growth of the market in the region.
  • North America commands the machine learning services over other key regions on account of rapid integration of machine learning as a service with big data, Internet of Things (IoT), and other advanced technologies. The region having headquarters of some of the to topnotch companies for machine learning as a service adds to its benefit.
  • Companies based in North America for instance, news organizations such as the Associated Press, are increasingly publishing new articles and stories written by software, instead of journalists. At the same time, Google is training its AI software, DeepMind, to detect two common types of eye diseases. Microsoft has been using AI for about a decade to improve its online search engine.

Competitive Landscape

The machine learning as a service market is highly competitive and consists of several major players. In terms of market share, few of the major players currently dominate the market. However, with the advancement of Artificial Intelligence, many of the companies are increasing their market presence by securing new contracts by tapping new markets.

Recent Developments

  • April 2019 - Microsoft developed a platform that uses machine teaching to help deep reinforcement learning algorithms tackle real-world problems. Microsoft scientists and product developers have pioneered a complementary approach called machine teaching. This relies on people's expertise to break a problem into easier tasks and give machine learning models important clues about how to find a solution faster.
  • November 2018 - Amazon announced the expansion of its machine learning services to healthcare organizations. Amazon Translate, Amazon Comprehend, and Amazon Transcribe are now HIPAA compliant.

Key Topics Covered


1.1 Study Deliverables

1.2 Study Assumptions

1.3 Scope of the Study




4.1 Market Overview

4.2 Introduction to Market Drivers & Restraints

4.3 Market Drivers

4.3.1 Increasing Adoption of IoT & Automation

4.3.2 Increasing Adoption of Cloud-based Services

4.3.3 Rising Demand of Digitalization across multiple End-User Segments

4.4 Market Restraints

4.4.1 Privacy & Data Security Concerns

4.4.2 Need for Skilled Professionals

4.5 Industry Attractiveness - Porter's Five Force Analysis

4.5.1 Threat of New Entrants

4.5.2 Bargaining Power of Buyers/Consumers

4.5.3 Bargaining Power of Suppliers

4.5.4 Threat of Substitute Products

4.5.5 Intensity of Competitive Rivalry


5.1 By Application

5.1.1 Marketing & Advertisement

5.1.2 Predictive Maintenance

5.1.3 Automated Network Management

5.1.4 Fraud Detection & Risk Analytics

5.1.5 Other Applications

5.2 By Organization Size

5.2.1 Small & Medium Enterprises

5.2.2 Large Enterprises

5.3 By End-user

5.3.1 IT & Telecom

5.3.2 Automotive

5.3.3 Healthcare

5.3.4 Aerospace & Defense

5.3.5 Retail

5.3.6 Government

5.3.7 BFSI

5.3.8 Other End-users

5.4 Geography

5.4.1 North America

5.4.2 Europe

5.4.3 Asia-Pacific

5.4.4 Rest of the World


6.1 Company Profiles

6.1.1 Microsoft Corporation

6.1.2 IBM Corporation

6.1.3 Google LLC

6.1.4 SAS Institute Inc.

6.1.5 Fair Isaac Corporation (FICO)

6.1.6 Hewlett Packard Enterprise Company

6.1.7 Yottamine Analytics LLC

6.1.8 Amazon Web Services Inc.

6.1.9 BigML Inc.

6.1.10 Iflowsoft Solutions Inc.

6.1.11 PurePredictive Inc.

6.1.12 Sift Science Inc.

6.1.13 Inc.



For more information about this report visit

Laura Wood, Senior Press Manager
For E.S.T Office Hours Call 1-917-300-0470
For U.S./CAN Toll Free Call 1-800-526-8630
For GMT Office Hours Call +353-1-416-8900
Related Topics: Machine Learning and Data Mining

Laura Wood, Senior Press Manager
For E.S.T Office Hours Call 1-917-300-0470
For U.S./CAN Toll Free Call 1-800-526-8630
For GMT Office Hours Call +353-1-416-8900
Related Topics: Machine Learning and Data Mining