The Global Automated Machine Learning Market (henceforth referred to as the market studied) was valued at USD 665. 63 Million in 2021, and it is expected to reach USD 5,406. 75 Million by 2027, registering a CAGR of 42.
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97% during the period of 2022-2027 (henceforth referred to as the forecast period).
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 subsequently drive decision-making within applications and businesses, ideally impacting key growth metrics. Since it revolves around algorithms, model complexity, and computational complexity, skilled professionals need to develop these solutions.
Machine learning (ML) has become an essential component of many parts of the business. Building high-performance machine learning applications, on the other hand, necessitates highly specialized data scientists and domain experts. The goal of automated machine learning (AutoML) is to decrease the need for data scientists 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 improvements in data science and artificial intelligence. 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 a wide array of 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 adoption of the technology 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.
Key Market Trends
BFSI Vertical to Drive the Market Growth
In recent years, AI and Machine 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 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 totally replace manual labor by automating repetitive operations through intelligent process automation, resulting in increased corporate productivity. Over the predicted 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 are increasingly looking to connect and serve their customers through digital channels. Chatbots, account-opening and handling assistance, and technical assistance, among others., are increasingly witnessed in the market. For instance, 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 markets in the region.
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 a 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 makes significant investments 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.
The global automated machine learning market is moderately fragmented, with the presence of several players in the market that are 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.
Conclusively, the market under study encompasses a high degree of competition and is expected to continue to remain highly competitive over the coming years.
December 2021 - Meta selected AWS as a key, long-term strategic cloud provider. Meta and AWS will work together to improve the performance of customers running PyTorch on AWS and accelerate how developers build, train, deploy, and operate artificial intelligence/machine learning models.
November 2021 - SAS added support for open-source users to its flagship SAS Viya platform. SAS Viya is for open-source integration and utility. The software user established an API-first strategy that fueled a data preparation process with machine learning?.
September 2021 - dotData, a full-cycle enterprise AI automation solutions provider, announced a partnership with Tableau, an analytics platform, to enable Tableau users to leverage the power of dotData’s AI Automation Capabilities. By combining Tableau’s data preparation and visualization capabilities with dotData’s augmented insights discovery and predictive modeling capabilities, Tableau users can perform full-cycle predictive analysis from raw data through data preparation and insight discovery through AI-based predictions and actionable dashboards.
The market estimate (ME) sheet in Excel format
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