Global Automated Machine Learning (AutoML) Market Size (2023 – 2030)

The Global Automated Machine Learning (AutoML) Market is estimated to be worth USD 951.65 Million in 2022 and is projected to reach a value of USD 16.61 Billion by 2030, growing at a CAGR of 42.97% during the forecast period 2023-2030.

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The market for automated machine learning (AutoML) is expanding quickly because it makes it possible for companies to create and use forecast models without having to hire a lot of data scientists. Many businesses rely heavily on machine learning (ML), but creating high-performance ML apps necessitates the expertise of highly specialised data scientists and subject matter experts. By allowing domain experts to build ML applications automatically without an in-depth understanding of statistics and ML, AutoML aims to lessen the need for such experts. The performance of AutoML has increased due to developments in data science and artificial intelligence, and as more businesses see its potential, its adoption rate is likely to rise. Customers have flexibility with pay-as-you-go pricing because AutoML solutions are offered on a subscription basis.

The Automated Machine Learning (AutoML) sector is one area of artificial intelligence (AI) that is expanding rapidly. Without the need for in-depth data science expertise, businesses and organisations can quickly create and implement predictive models using AutoML, a technique for automating the development of machine learning models. The growing use of AI and machine learning across a variety of industries is another factor driving the AutoML industry. AutoML is being used, for instance, in the healthcare sector to create prognostic models for the identification and management of diseases. AutoML is also used in the financial sector for risk analysis and fraud discovery. Demand predictions and individualised marketing are two retail-related applications of AutoML. 

Global Automated Machine Learning (AutoML) Market Drivers:

Increased demand for AI and machine learning solutions is driving the Automated ML market

The worldwide AutoML market is being driven by the rising demand for AI and machine learning solutions across numerous industries. Machine learning has become a potent instrument for automating prediction and decision-making processes as well as for gleaning insights from the recent explosion of data. For businesses lacking the data science knowledge or resources to develop machine learning models internally, the platforms present an alluring option. AutoML is used in the healthcare sector to identify and cure diseases, and in the financial sector to identify fraud and evaluate risk. The demand for AutoML platforms is anticipated to increase as more sectors implement AI and machine learning.

Cloud-based AutoML platforms are fuelling the demand for Automated Machine Learning

Another important factor propelling the global AutoML market is the rising acceptance of cloud-based AutoML systems. These platforms give users access to machine learning tools and resources from any location with an internet link because they are SaaS solutions. Cloud-based AutoML platforms are less expensive up front, simpler to scale, and require less maintenance than on-premise solutions. Additionally, they encourage innovation in the market for AutoML, where vendors are constantly introducing new features and capabilities that weren't accessible before. Cloud-based AutoML platforms present a compelling alternative for companies and groups lacking the resources or knowledge to maintain their infrastructure. The demand for cloud-based AutoML platforms would rise further as cloud computing gains prominence.

Global Automated Machine Learning (AutoML) Market Challenges:

The lack of interpretability and transparency in automated machine learning models is a significant issue for the worldwide AutoML industry. Contrary to traditional machine learning models, which data scientists can examine and describe, AutoML models frequently function as a "black box," making it challenging to comprehend how they make predictions or recommendations. Businesses and organisations may find it challenging to embrace and act upon the insights generated by AutoML models due to their lack of transparency and interpretability. It might be challenging for officials and data scientists to guarantee the models' objectivity and morality. In order for the business to move forward, it must focus on overcoming the major challenge of improving the transparency and interpretability of AutoML models.

Global Automated Machine Learning (AutoML) Market Opportunities:

The potential for growth into emerging markets like Asia-Pacific and Latin America represents one of the biggest opportunities for the global AutoML industry. These areas are rapidly undergoing a digital transition and adopting AI and ML solutions at a higher rate, which presents significant growth opportunities for AutoML vendors. Additionally, there is a rising need for low-code and no-code AI solutions that can be quickly implemented and handled by companies with little to no data science experience, opening up opportunities for AutoML vendors to broaden their product portfolios and seize new market shares. 

COVID-19 Impact on the Global Automated Machine Learning (AutoML) Market:

COVID-19 has had a mixed impact on the global AutoML industry. The pandemic had some positive effects, like accelerating digital transformation and increasing demand for AI and machine learning solutions across many sectors. Businesses tried to automate forecasting and judgment processes, which increased the use of AutoML systems. The pandemic adversely affected supply lines and forced businesses to cut costs, which reduced IT budgets and slowed the adoption of new technologies. The pandemic also highlighted the need for moral and open AI solutions, which slowed down the adoption of AutoML platforms with weak interpretability and openness.

Global Automated Machine Learning (AutoML) Market Recent Industry Developments:

• In February 2022, Amazon Web Services (AWS) completed its first 16 AWS Local Zones in the U.S. and is planning to launch 32 new AWS Local Zones in 26 countries worldwide. AWS Local Zones are a type of infrastructure deployment that places computing, storage, and other AWS services near population centers. This enables customers to deploy applications with single-digit millisecond latency closer to end users or on-premise data centers.
• In December 2021, six new Amazon SageMaker features were been announced by AWS to increase machine learning's usability and affordability. A no-code environment for making predictions, more precise data labelling, universal Studio notebook support, a compiler for training, automatic instance selection, and serverless computing for inference are some of the new features. These attributes will contribute to the affordability and accessibility of machine learning.

AUTOMATED MACHINE LEARNING (AUTOML) MARKET REPORT COVERAGE:

Market Segmentation Analysis

Global Automated Machine Learning (AutoML) Market Segmentation: By Solution

• Standalone
• On-Premises

The market for the global Automated Machine Learning (AutoML) industry can be divided into two categories, standalone and on-premises solutions. Standalone solutions are more common among small and medium-sized companies because they are cloud-based, offer benefits like lower upfront costs, easier scalability, and require less upkeep. On the other hand, on-premises solutions must be maintained and supported by internal IT teams because they are installed directly. Although standalone AutoML solutions are anticipated to rule the market due to their convenience, bigger businesses that have more sophisticated IT capabilities and may need to meet regulatory or security requirements favour on-premises solutions. Despite this, it is anticipated that cloud-based solutions will continue to gain recognition and market share. On-premises solutions, however, are anticipated to maintain a significant market share, particularly among bigger businesses with complex IT environments.

Global Automated Machine Learning (AutoML) Market Segmentation: By Automation Type

• Feature Engineering
• Data Processing
• Data Modelling
• Visualization
• Others

By automation style, the market for automated machine learning (AutoML) can be divided into feature engineering, data processing, data modelling, visualisation, and others. Due to its essential part in machine learning, as well as its potential to reduce waiting times and boost productivity, data processing is predicted to dominate the market. As essential elements in creating precise and efficient machine learning models, feature engineering and data modelling are also anticipated to have a sizable market share. Although the visualisation market is predicted to have a smaller market share, it is still a crucial part of AutoML because it makes it simpler to understand and communicate model findings. Overall, based on the unique requirements and priorities of each business, the market share for each segment may change.  

Global Automated Machine Learning (AutoML) Market Segmentation: By End-User

• BFSI
• Retail and E-Commerce
• Healthcare
• Manufacturing
• Others

The BFSI sector has embraced AI and machine learning more frequently to boost operational effectiveness and enhance customer experience. As data receives more focus, there is an increasing demand for machine learning BFSI applications. Large amounts of data, reasonably priced processing capacity, and cost-effective storage are used in automated machine learning to produce precise and quick results. Businesses can work together with other fintech services to adjust to contemporary requirements and laws while enhancing safety and enabling security. Finance companies can automate repetitive tasks through intelligent process automation with the help of machine learning-powered solutions, which increases output. Due to its adoption of AI and machine learning solutions for fraud detection, risk management, and customer service, the BFSI sector leads the AutoML market. Retail and online sales come next, with a rise in the use of customised marketing and supply chain optimization, and customer service. AutoML is widely used in the healthcare industry for drug discovery, patient diagnosis, and therapy planning. Supply chain optimization, predictive maintenance, and quality control are all areas where the industrial industry makes use of AI and machine learning solutions. Slower adoption of AutoML solutions is anticipated in other sectors like government, transit, and education. Overall, market share varies based on particular requirements and priorities for each end-user segment. By spotting red flags like money laundering techniques, machine learning algorithms also significantly increase network security.

Global Automated Machine Learning (AutoML) Market Segmentation: By Region

• North America
• Europe
• Asia Pacific
• Middle East
• Latin America

Due to the rising IT spending and FinTech adoption, the Asia Pacific (APAC) area is predicted to have the fastest-growing market for AutoML. Governments in APAC are also eager to incorporate AI into a number of sectors, promoting the growth of local markets. Machine learning adoption is increasing significantly in China, where businesses are using the technology for financial fraud detection, product recommendations, and industrial process optimization. However, dependable infrastructure and clean data are essential for machine learning initiatives to succeed. Due to the demand for AI in robotics, speech recognition, and visual recognition on a worldwide scale, Japan's AI industry is anticipated to expand. South Korea makes significant investments in cutting-edge technologies like AI and ML, which helps the industry expand. North America, Europe, Asia Pacific, the Middle East, and Latin America are the regions into which the global AutoML industry can be divided. The market is anticipated to be dominated by North America, but Europe is also anticipated to account for a sizeable portion of the market thanks to investments in AI and machine learning technologies and the rising acceptance of cloud-based solutions. Based on economic, political, and technological variables, it is likely that each region's market share will vary, with some favouring investment in AI and machine learning solutions while others do so for regulatory and security reasons. 

Global Automated Machine Learning (AutoML) Market Key Players:

1. Datarobot inc.
2. Amazon web services Inc.
3. dotData Inc.
4. IBM Corporation
5. Dataiku
6. SAS Institute Inc.
7. Microsoft Corporation
8. Google LLC
9. H2O.ai
10. Aible Inc.