Automated Machine Learning Market Size & Share Analysis - Growth Trends & Forecasts (2023 - 2028)

Automated Machine Learning Market Size & Share Analysis - Growth Trends & Forecasts (2023 - 2028)

The Automated Machine Learning Market size is estimated at USD 1.24 billion in 2023, and is expected to reach USD 7.42 billion by 2028, growing at a CAGR of 42.97% during the forecast period (2023-2028).

Key Highlights

• Machine learning (ML) is a subfield of artificial intelligence (AI) that enables training algorithms to make classifications or predictions through statistical methods, uncovering key insights within data mining projects. These insights drive decision-making within applications and businesses, ideally impacting key growth metrics. Since it revolves around algorithms, models, and computational complexity, skilled professionals must develop these solutions.
• Machine learning (ML) has become an essential component of many parts of the business. On the other hand, building high-performance machine learning applications necessitates highly specialized data scientists and domain experts. Automated machine learning (AutoML) aims to decrease data scientists' needs by allowing domain experts to automatically construct machine learning applications without considerable knowledge of statistics and machine learning.
• The performance of automated machine learning has advanced due to data science and artificial intelligence improvements. Companies recognize the potential of this technology, and hence its adoption rate is likely to rise over the forecast period. Companies are selling automated machine learning solutions on a subscription basis, making it easier for customers to use this technology. Furthermore, it offers flexibility on a pay-as-you-go basis.
• Machine learning (ML) is increasingly used in many applications, but there are insufficient machine learning experts to support this growth adequately. With automated machine learning (AutoML), the aim is to make machine learning easier to use. Therefore, experts should be able to deploy more machine learning systems, and less expertise would be needed to work with AutoML than when working with ML directly. However, the technology adoption is still shallow, restraining the market's growth.
• The adoption of AI is witnessing an increase after the COVID-19 pandemic as companies move towards leveraging intelligent solutions for automating their business processes. This trend is expected to continue over the coming years, further driving the adoption of AI in organizational processes.

Automated Machine Learning Market Trends

BFSI Vertical to Drive Market Growth

• Recently, AI and ML technologies have been increasingly adopted in the BFSI industry to enhance operational efficiency and improve the consumer experience. As data gain more attention, the demand for machine learning in BFSI applications grows. Automated machine learning can produce accurate and rapid results with enormous data, affordable processing power, and economical storage. In addition, the machine learning-led approach to system modernization will allow businesses to collaborate with other fintech services to adapt to modern demands and regulations while increasing safety and enabling security.
• Banks must enhance their services to offer better customer service with the rising pressure in managing risk and increasing governance and regulatory requirements. Some fintech brands have been increasingly using AI and ML in various applications across multiple channels to leverage available client data and predict how customers’ needs are evolving, which fraudulent activities have the highest possibility of attacking a system, and what services will prove beneficial, among others.
• Machine learning-powered solutions enable finance firms to replace manual labor by automating repetitive operations through intelligent process automation, resulting in increased corporate productivity. Over the forecast period, examples include chatbots, paperwork automation, and employee training gamification. Machine learning is being used to automate financial processes.
• Amid the COVID-19 pandemic, financial institutions increasingly seek to connect and serve customers through digital channels. Chatbots, account opening and handling assistance, and technical assistance, among others, are increasing in the market. Posh. Tech, Spixii, and many other fintech companies offer intelligent chatbots for critical customer-facing processes to banks.​
• Automated Machine learning (ML) algorithms can significantly improve network security. Data scientists have been working on training systems to detect flags, such as money laundering techniques, which can be prevented by financial monitoring. The future holds a high possibility of machine learning technologies powering the most advanced cybersecurity networks.

Asia-Pacific to Witness Significant Growth in the Market

• Asia-Pacific (APAC) is considered the fastest-growing market region in the coming years. This is due to increased investment in information technology (IT) and increased adoption of FinTech in the area. In addition, growing government interest in integrating AI into multiple industries is helping to develop regional markets.
• Machine learning is gaining momentum in China, and companies are using this technology to detect financial fraud, recommend products to consumers, and streamline industrial operations. Many machine learning projects fail due to inaccurate predictions made by machine learning algorithms that are not backed up by clean data and robust data infrastructure.
• The rise of AI has been made possible by exponentially fast and powerful computers and large, complex datasets. Applications such as machine learning, where the system identifies patterns in large datasets, prove AI's practical and profitable potential. In China, with the ability of AI systems to monitor public spaces and scan internet traffic to determine user intent, the state provides enhanced automated machine learning tools for social control, monitoring, or censoring the population.
• The increasing global demand for AI, especially in robotics, speech recognition, and visual recognition, is expected to boost the Japanese AI market. Further, the Rakuten Institute of Technology (RIT) in Japan focuses primarily on automated machine learning and deep learning, covering IoT, network optimization, fraud detection, NLP, computer vision, and virtual reality.
• South Korea is a significantly developed nation. Moreover, the country invests significantly in developing advanced technologies, such as AI and ML. Various companies operating across the nation are getting investments from various sources that aid the market's growth.

Automated Machine Learning Industry Overview

The global market for automated machine learning is moderately fragmented, with several players catering to the market demand. The market is increasingly getting competitive as several new players are entering the market. As such, the strategies adopted by existing players to capture a greater number of customers, coupled with the emergence of new players, are increasing the competition in the market.

The market studied is highly competitive and is expected to remain highly competitive over the coming years.

April 2023: San Mateo-based Yellow AI announced the release of its generative AI-driven Dynamic Automation Platform (DAP). To ensure scalability, speed, and accuracy, the DAP is built on a multi-LLM architecture that is continually trained on billions of talks. By automating staff and consumer interactions across channels, the platform's generative AI drastically lowers operational expenses.

November 2022: Infosys partnered with IBM to launch the center of AI and automation. This center will highlight an expanding range of data and AI solutions created to streamline and quicken the transition of multinational corporations to hybrid clouds. The center will enhance Infosys BPM service offerings, developed using a design-thinking methodology and extensive industry knowledge of data and AI technologies.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support