Global Automated Machine Learning Market Size Grows Tremendously at a CAGR of 42.77%

Introduction

Automated machine learning (AutoML) is a process that automates a few of the more complex or benign stages of the machine-learning lifecycle. This enables those without a theoretical or practical understanding of machine learning to contribute to the development of artificial intelligence. Applying conventional machine learning techniques to real-world business problems is difficult, resource-intensive, and time-consuming. It requires experts in various fields, including data scientists, among the most in-demand professionals on the market today.

Market Dynamics

Increasing Demand for Efficient Fraud Detection Solutions Drives the Global Market

Financial fraud is one of the most serious threats to financial security. In order to combat the rising risk of financial fraud, fraud detection applications extensively use machine learning. Several financial services industry players are increasingly adopting AI and ML into their ecosystems to leverage the vast data available from newly acquired digital channels.

As businesses continue to adopt online credit card payments, there is a growing need for a fraud detection solution that can generate real-time, actionable alerts. As per the Federal Trade Commission's (FTC) Annual Data Book, credit cards were the most frequently reported payment method for fraud in 2020, with 459,297 reported fraud and identity theft cases. Increasing demand for effective fraud detection solutions, therefore, drives market expansion.

Adoption of Cloud-Based AutoML Systems Creates Tremendous Opportunities

The increasing acceptance of cloud-based AutoML systems is a major factor propelling the global AutoML market. As SaaS offerings, these platforms provide users access to machine learning tools and resources from any Internet-connected location. Cloud-based AutoML platforms are initially more affordable and scalable and require less maintenance than on-premise solutions.

In addition, they promote innovation in the AutoML market, where vendors continually introduce previously unavailable features and capabilities. Cloud-based AutoML platforms entice businesses and organizations without the resources or knowledge to maintain their infrastructure. As cloud computing gains prominence, demand for cloud-based AutoML platforms will increase, creating opportunities for market expansion.

Regional Analysis

North America is the most significant global automated machine learning market shareholder and is anticipated to exhibit a CAGR of 42.17% during the forecast period. The region is expected to maintain its dominance due to its robust innovation ecosystem, fueled by strategic federal investments in advanced technology and complemented by visionary scientists and entrepreneurs from around the world, as well as renowned research institutions. Increased commercial use of federatedML is anticipated to further stimulate demand for autoML. According to Helpnetsecurity, 73% of U.S. businesses intend to increase their use of artificial intelligence and machine learning (AI/ML) in their cybersecurity tools in 2022.

In addition, in January 2021, the FDA released an action plan to develop a coordinated strategy for enhancing AI/ML focus. The scientific and empirical advancements in digital health technologies primarily drove this. To accomplish this, the FDA has outlined plans to build an ML-supporting infrastructure to evaluate and enhance healthcare algorithms. Thus, these factors are anticipated to stimulate market expansion.

Europe is estimated to exhibit a CAGR of 42.47% over the forecast period. Europe is home to some of the largest pharmaceutical companies in the world, as well as an increasing number of healthcare AI startups working on everything from drug development to hospital workforce logistics. AutoML is required to automate the various data models collected by AI and the processes in health care as the deployment of AI and ML in tandem increases. For example, Merantix, an AI research and incubator lab based in Germany, is developing a cloud-based, on-demand platform to make its cancer-detection AI accessible to radiologists worldwide. The increase in digital marketing expenditures in the region also creates new opportunities for automated machine learning.

Key Highlights

• The global automated machine learning market size was valued at USD 701.60 million in 2022. It is estimated to reach USD 17,291.08 million by 2031, growing at a CAGR of 42.77% during the forecast period (2023–2031).
• Based on the solution, the global automated machine learning market is divided into standalone, on-premise, and cloud. The standalone or on-premise segment is the largest revenue contributor to the market and is expected to exhibit a CAGR of 41.24% throughout the forecast period.
• Based on automation type, the global automated machine learning market is bifurcated into data processing, feature engineering, modeling, and visualization. The visualization segment dominates the global market and is projected to exhibit a CAGR of 41.88% over the forecast period.
• Based on the end-user, the global automated machine learning market is segmented into BFSI, retail and e-commerce, healthcare, and manufacturing. The BFSI segment owns the highest market share and is estimated to exhibit a CAGR of 41.51% during the forecast period.
• North America is the most significant global automated machine learning market shareholder and is anticipated to exhibit a CAGR of 42.17% during the forecast period.

Competitive Players

Competitive Players

The key global automated machine learning market players are Datarobot Inc., dotData Inc., Amazon Web Services Inc., IBM Corporation, Dataiku, Google LLC, SAS Institute Inc., Microsoft Corporation, H2O.ai, and Aible Inc.

Recent Developments

Market News

• In March 2023, HUMBL launched its Artificial Intelligence (AI) and Automated Machine Learning initiatives for its consumer, commercial, and Latin American business units.
• In September 2023, Fujitsu Limited and the Linux Foundation officially released Fujitsu's automated machine learning and AI fairness technologies as open-source software (OSS) before the "Open Source Summit Europe 2023," held in Bilbao, Spain.

Segmentation

Global Automated Machine Learning Market: Segmentation

By Solution

• Standalone or On-Premise
• Cloud

By Automation Type

• Data Processing
• Feature Engineering
• Modeling
• Visualization

By End-Users

• BFSI
• Retail and E-Commerce
• Healthcare
• Manufacturing
• Other Users

By Regions

• North America
• Europe
• Asia-Pacific
• Rest of the World