|Fastest Growing Market:||Asia-Pacific|
|Largest Market:||North America|
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The Global Automated Machine Learning Market (hereafter 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 42.97% from 2022 to 2027 (hereafter 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 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 adequately support this growth. 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.
Scope of the Report
Automated Machine Learning or AutoML refers to a process of automating the time-consuming, iterative tasks of machine learning model development. It allows data scientists, developers, and analysts to build large-scale, productive, and efficient ML models while sustaining model quality.
The Global Automated Machine Learning Market is segmented By Solution (Standalone or On-Premise, and Cloud), By Automation Type (Data Processing, Feature Engineering, Modeling, and Visualization), and By End-users (BFSI, Retail and E-Commerce, Healthcare, and Manufacturing), and Geography.
|Standalone or On-Premise|
|Retail and E-Commerce|
|Rest of the World|
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 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 increasingly seek 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.
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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 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 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.
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The global automated machine learning market is moderately fragmented, with several players in the market 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 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 - dot data, 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.
- January 2022 - AWS announced the global expansion of AWS local zones. It announced the completion of its first 16 AWS Local Zones in the US and planned to launch new AWS Local Zones in 32 new metropolitan areas in 26 countries worldwide.
- December 2021 - AWS announced six new Amazon SageMaker capabilities. This will make machine learning even more accessible and cost-effective. This brings together powerful new capabilities, including a no-code environment for creating accurate machine learning predictions and more accurate data labeling using highly skilled annotators.
Table of Contents
1.1 Study Assumptions and Market Definition
1.2 Scope of the Study
2. RESEARCH METHODOLOGY
3. EXECUTIVE SUMMARY
4. MARKET DYNAMICS
4.1 Market Drivers
4.1.1 Increasing Demand For Efficient Fraud Detection Solutions
4.1.2 Growing Demand For Intelligent Business Processes
4.2 Market Restraints
4.2.1 Slow Adoption of Automated Machine Learning Tools
4.3 Industry Value Chain Analysis
4.4 Industry Attractiveness - Porter's Five Forces Analysis
4.4.1 Threat of New Entrants
4.4.2 Bargaining Power of Buyers
4.4.3 Bargaining Power of Suppliers
4.4.4 Threat of Substitute Products
4.4.5 Intensity of Competitive Rivalry
4.5 Assessment of the Impact of COVID-19 on the Market
5. MARKET SEGMENTATION
5.1.1 Standalone or On-Premise
5.2 Automation Type
5.2.1 Data Processing
5.2.2 Feature Engineering
5.3.2 Retail and E-Commerce
5.4.1 North America
22.214.171.124 United States
126.96.36.199 United Kingdom
188.8.131.52 Rest of Europe
184.108.40.206 South Korea
220.127.116.11 Rest of Asia-Pacific
5.4.4 Rest of the World
6. COMPETITIVE LANDSCAPE
6.1 Company Profiles*
6.1.1 Datarobot inc.
6.1.2 Amazon web services Inc.
6.1.3 dotData Inc.
6.1.4 IBM Corporation
6.1.6 SAS Institute Inc.
6.1.7 Microsoft Corporation
6.1.8 Google LLC
6.1.10 Aible Inc.
7. INVESTMENT ANALYSIS
8. FUTURE OF THE MARKET
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Frequently Asked Questions
What is the study period of this market?
The Automated Machine Learning Market market is studied from 2020 - 2027.
What is the growth rate of Automated Machine Learning Market?
The Automated Machine Learning Market is growing at a CAGR of 42.97% over the next 5 years.
Which region has highest growth rate in Automated Machine Learning Market?
Asia-Pacific is growing at the highest CAGR over 2021- 2026.
Which region has largest share in Automated Machine Learning Market?
North America holds highest share in 2021.
Who are the key players in Automated Machine Learning Market?
Datarobot Inc., Amazon Web Services Inc., dotData Inc., IBM Corporation, Dataiku are the major companies operating in Automated Machine Learning Market.