Automated Machine Learning Market Size, Share & Trend Analysis Report By Offering (Solution, Services), By Enterprise Size, By Application, By Vertical, By Region, And Segment Forecasts, 2024 - 2030

AutoML Market Size & Trends

The automated machine learning market size was estimated at USD 2,658.9 million in 2023 and is projected to grow at a CAGR of 42.2% from 2023 to 2030. This growth is attributed to the AutoML’s capability to identify discrepancies, errors, and other issues within the data, and present the user with choices, suggestions as well as suggest outliers. Once the expert is presented with all this information, they can seamlessly curate multiple models, saving them time and effort.

Currently, automated machine learning (AutoML) open-source and commercial tools such as TPOT, H2O.ai, Google AutoML, and DataRobot are some of the best suited for streamlining the development of tasks wherein the goal is to predict an outcome/ result. These popular solutions tend to automate some or all the ML pipelines. For instance, DataRobot, the enterprise AI platform, makes data science accessible to everyone and automates the entire process of creating, deploying, and managing AI solutions at scale. It eliminates the reliance on manual workflows, automates repetitive and time-intensive steps, enables new users to build highly accurate models, and provides a fast-path for getting AI into production.

Automated Machine Learning (AutoML) is an essential process of automating iterative and time-consuming tasks. It enables developers, analysts, and data scientists to build ML models with productivity, efficiency, and high scale. AutoML has gained traction to minimize the knowledge-based resources needed to implement and train machine learning models. Moreover, the bullish demand for AutoML is mainly attributed to its ability to help enterprises boost insights and enhance model accuracy by minimizing chances for error or bias. End users, including BFSI, healthcare, IT & telecom and retail, are expected to inject funds into AutoML to rev up their AI efforts to create a valuable pipeline to automate data preprocessing, model selection and pre-trained models.

Market Concentration & Characteristics

Innovation in automated machine learning has led to significant advancements in various industries, transforming the way businesses operate and interact with their customers. Automation of complex processes enables organizations to speedily analyze network behavior and automatically execute required steps, enhancing processing speeds and performance. Additionally, predictive maintenance using machine learning helps companies identify potential risks and predict failures, thereby increasing productivity and saving costs. Real-time business decision making is also facilitated through machine learning, allowing businesses to extract valuable insights from large datasets and make informed decisions. TinyML, a type of machine learning that runs on smaller devices, is ideal for battery-operated devices and IoT applications, reducing power consumption, latency, and bandwidth while maintaining user privacy and efficiency.

The automated machine learning market is characterized by a high level of merger and acquisition (M&A) activity. For instance, in June 2023, Accenture acquired Nextira, a premier partner of Amazon Web Services (AWS) that leverages AWS to provide cloud-native innovation, predictive analytics, and immersive experiences for its clients. These services and solutions will enhance Accenture Cloud First's extensive engineering capabilities, enabling clients to fully utilize a comprehensive range of cloud tools and features.

In the context of automated machine learning, service substitutes refer to tools or platforms that automate the process of building and training ML models. These tools simplify the machine learning workflow and make it more accessible to non-experts. Aible is a suite of AI solutions that automates data science and data engineering tasks across multiple industries, detecting key data relationships and assessing data readiness for model input. AutoKeras is an open-source library and autoML tool based on Keras, automating classification and regression tasks in deep learning models for images, text, and structured data. Auto-PyTorch is another autoML tool based on the PyTorch machine learning library in Python, automating algorithm selection and hyperparameter tuning for deep neural network architectures and supporting tabular and time series datasets. Auto-Sklearn is an open-source autoML tool built on the scikit-learn machine learning library in Python, automating supervised machine learning pipeline creation and serving as a drop-in replacement for scikit-learn classifiers. Google Cloud AutoML is a suite of autoML tools developed by Google, allowing users to create custom machine learning models for objectives such as classification, regression, and forecasting in image, video, text, and tabular data. These service substitutes in AutoML aim to simplify the machine learning process by automating tasks such as data preprocessing, model selection, and hyperparameter tuning, accelerating innovation and improving efficiency in various industries.

The end user concentration for automated machine learning is diverse and spans across various industries. The BFSI sector is one of the largest adopters of AutoML, with applications in fraud detection, risk management, and customer service. Retailers are leveraging AutoML to enhance customer experiences, optimize inventory management, and improve supply chain efficiency. Healthcare organizations are adopting AutoML to improve patient outcomes, streamline clinical workflows, and enhance disease diagnosis. Manufacturers are using AutoML to optimize production processes, predict equipment failures, and improve product quality. Additionally, other industries such as government, education, and transportation are also adopting AutoML to improve their operations. For instance, government agencies use AutoML for fraud detection and public safety, while educational institutions use it to personalize learning experiences.

Offering Insights

The services segment led the market and accounted for 52.4% of the global revenue in 2023. Automated machine learning services aim to simplify and automate various stages of the machine learning workflow, making it more accessible to users without extensive expertise in data science and machine learning. These services automate the process of building machine learning models, including tasks like data preprocessing, feature engineering, algorithm selection, and hyperparameter tuning, allowing users to focus on the problem they want to solve rather than the intricacies of model development.

Major cloud providers such as Google Cloud, Amazon Web Services, and Microsoft Azure offer fully managed AutoML services on their cloud platforms, providing a user-friendly interface and handling the underlying infrastructure. There are also open-source AutoML libraries like Auto-Sklearn, AutoKeras, and Auto-PyTorch that automate ML tasks for specific frameworks. Some AutoML services aim to automate the entire machine learning lifecycle, from data ingestion and preparation to model deployment and monitoring, significantly reducing the time and effort required. Additionally, there are vertical-specific solutions tailored to industries or use cases like healthcare, manufacturing, or computer vision, leveraging domain knowledge and pre-trained models.

The solution segment is estimated to grow significantly over the forecast period. AutoML solutions are designed to automate the tasks involved in developing and deploying machine learning models. This makes it easier for organizations to leverage the power of machine learning without requiring significant expertise in data science or machine learning. AutoML solutions are becoming an increasingly important tool for organizations looking to leverage the power of machine learning to gain insights from their data and make better decisions. By automating many tedious and time-consuming tasks involved in model development and deployment, AutoML platforms can help organizations accelerate their digital transformation and unlock new opportunities for growth and innovation.

Enterprise Size Insights

The large enterprises segment dominated the market in 2023. Based on enterprise size, the automated machine learning market is categorized into Small and Medium Enterprises (SMEs) and large enterprises. Large businesses are increasingly adopting cloud-based AutoML platforms and services. The scalable and cost-effective infrastructure of cloud platforms facilitates the training and deployment of machine learning models. Services like Amazon Web Services (AWS), Google Cloud AI Platform, and Microsoft Azure Machine Learning provide pre-built models, distributed training capabilities, and infrastructure management, enabling large enterprises to utilize automated machine learning without substantial infrastructure investments.

The small & medium size enterprises segment is projected to grow significantly over the forecast period. The adoption of machine learning is rapidly growing among small and medium-sized enterprises (SMEs). With often limited resources, SMEs may need extra expertise to analyze large data sets. Machine learning platforms and technologies can automate data analysis processes, allowing SMEs to gain valuable insights from their data with minimal manual effort. This automated data analysis helps SMEs better understand customer behavior, improve inventory management, optimize marketing strategies, and make data-driven decisions. 

Deployment Insights

The Cloud segment accounted for the largest market revenue share in 2023. Cloud-based AutoML solutions have gained significant traction in recent years, offering businesses and organizations a convenient and scalable way to leverage automated machine learning capabilities. These solutions, such as Google Cloud AutoML, Amazon SageMaker Autopilot, and Azure AutoML, provide user-friendly interfaces and abstraction layers that simplify the process of building and deploying machine learning models, enabling users with limited machine learning expertise to leverage advanced AutoML capabilities. Additionally, cloud platforms offer virtually unlimited computing resources that can be dynamically scaled up or down based on demand, ensuring optimal performance and cost-effectiveness for AutoML workloads.

The on-premises segment is predicted to foresee significant growth in the forecast period. On-premises based AutoML solutions offer organizations the ability to leverage automated machine learning capabilities within their own infrastructure and data centers. One of the primary advantages of on-premises AutoML is the ability to keep sensitive data within the organization’s-controlled environment, which is particularly important for industries dealing with sensitive information, such as healthcare, finance, and government, where data privacy and compliance regulations are stringent. Additionally, on-premises AutoML solutions provide greater control and customization options, allowing them to tailor the platform to their specific needs, integrate it with existing systems and workflows, and ensure compatibility with their infrastructure and security protocols.

Application Insights

The Data Processing segment held a leading revenue share of the market in 2023. Automated machine learning can be utilized to automate various aspects of data processing, such as data cleaning, normalization, and transformation. The automated machine learning marketstreamlines the process of identifying and correcting data errors, including detecting missing values, fixing data formatting issues, and removing outliers that could impact the accuracy of machine learning models. AutoML employs techniques like standardization and normalization automatically. It can also transform data into more suitable formats, minimizing the risk of errors and inconsistencies. Additionally, AutoML can integrate data from multiple sources, a typically time-consuming and complex task, through techniques like data merging and joining. By automating these tasks, AutoML significantly reduces the time and effort needed for manual data processing, enhancing the quality and accuracy of the resulting data.

The feature engineering segment is projected to grow significantly over the forecast period. Feature engineering is a crucial step in automated machine learning (AutoML) pipelines, as it significantly impacts the performance of the resulting models. AutoML tools and libraries like FeatureTools, Dask-ML, and TSFRESH can automatically generate new features from raw data by applying techniques like feature synthesis, feature extraction, and feature construction, reducing the manual effort required for feature engineering. For datasets consisting of data from multiple related tables or sources, AutoML tools can automatically join and combine data from these sources to generate meaningful features that capture relationships across different entities.

Vertical Insights

The BFSI segment accounted for a leading revenue share of the market in 2023. In recent years, artificial intelligence (AI) and machine learning technologies have been increasingly adopted in the banking, financial services, and insurance (BFSI) industry to boost operational efficiency and enhance the consumer experience. As data becomes more prominent, the demand for machine learning applications in the BFSI sector continues to grow. Automated machine learning can deliver accurate and swift results using vast amounts of data, affordable processing power, and cost-effective storage. Additionally, machine learning (ML)-powered solutions enable financial firms to enhance productivity by automating repetitive tasks through intelligent process automation.

The IT & Telecommunications segment is projected to grow significantly over the forecast period. Machine learning models can analyze millions of data points related to device mobility, call traffic, demographic trends, and application usage to make precise decisions on where to upgrade, densify, and optimize network infrastructure. This enables data-driven network planning, moving beyond solely relying on expert estimations.

Regional Insights

North America accounted for holding a significant share of a 30.7% share in 2023. This region has been a major contributor to the development and growth of the automated machine learning market. The U.S. is one of the most developed countries in the region. AutoML is a rapidly growing market in the U.S., with several key players offering solutions that range from fully automated platforms to ones that assist data scientists in building machine learning models. The market is being driven by the need for faster and more efficient ways to build and deploy machine learning models, as well as the increasing demand for artificial intelligence solutions in various industries. In recent years, there has been a significant increase in the adoption of AutoML solutions in the U.S., especially in industries such as healthcare, finance, and retail. Healthcare providers are using AutoML to analyze medical images and identify patterns in patient data, while financial institutions are using it to detect fraudulent transactions and assess credit risk. Retailersare using AutoML to personalize recommendations and improve customer engagement.

U.S. Automated Machine Learning Market Trends

The U.S. is at the forefront of automated machine learning research and development, with major tech companies, academic institutions, and businesses actively investing in and adopting AutoML solutions. Major U.S. tech giants like Microsoft, Google, and Amazon are investing heavily in developing AutoML solutions and offering cloud-based AutoML services. Microsoft Azure offers Azure AutoML, which automates the end-to-end machine learning process, including data preprocessing, model selection, hyperparameter tuning, and model deployment, making AutoML accessible to users without extensive machine learning expertise. Google Cloud provides the AutoML suite of tools, which includes pre-trained models for tasks like image recognition, text classification, and structured data analysis, aiming to democratize machine learning by simplifying the model development process. Amazon Web Services (AWS) offers Amazon SageMaker Autopilot, an AutoML solution that automates data preprocessing, model tuning, and deployment, allowing businesses to quickly build and deploy machine learning models without extensive coding.

Europe Automated Machine Learning Market Trends

Automated machine learning in Europe has gained significant traction with various academic institutions, research organizations, and companies actively contributing to its development and adoption. Moreover, Europe has several leading academic institutions conducting research on AutoML techniques and methodologies. For instance, the University of Leiden in the Netherlands offers courses on AutoML, covering topics like hyperparameter optimization, meta-learning, and transfer learning. Additionally, the University of Freiburg in Germany has a dedicated research group focused on AutoML and meta-learning. These academic efforts contribute to advancing the theoretical foundations and practical applications of AutoML.

The automated machine learning market in the UK is actively involved in the development and adoption of automated machine learning technologies. Several companies, academic institutions, and research organizations within the UK are contributing to the advancement of AutoML. The UK is home to several companies offering AutoML solutions and services. For instance, Amazon's UK website promotes its "Automated Machine Learning in Action" book, which covers optimizing machine learning pipelines with automation components and tools like AutoKeras and KerasTuner. Additionally, companies like Beckhoff, based in the UK, offer machine learning solutions seamlessly integrated into their industrial automation software, TwinCAT 3. These solutions aim to make machine learning more accessible and scalable for industrial applications.

France automated machine learning market is expected to grow significantly over the forecast period. AutoML is gaining traction in France, with several companies and research institutions actively contributing to its development and adoption. According to the search results, France is home to numerous machine learning companies and startups, some of which are likely focused on AutoML solutions. One prominent player in the French AutoML landscape is AKUR8, a company that provides an automated machine learning solution for the insurance industry. Their platform aims to streamline the model building process, enabling insurers to create and deploy machine learning models more efficiently.

The automated machine learning market in Germany held the largest share of the Europe market. The University of Freiburg is home to the Machine Learning Lab, which focuses on the progressive automation of machine learning (AutoML) to democratize access to ML solutions. The lab develops AI systems that can build and improve AI models, with particular interests in meta-learning, neural architecture search, efficient hyperparameter optimization, and deep learning for tabular data. The lab co-organizes the MOOC on AutoML and has won several AutoML competitions.

Asia Pacific Automated Machine Learning Trends

Asia Pacific is anticipated to register the fastest CAGR over the forecast period. Various government initiatives are propelling the demand for automated machine learnings in Asia Pacific. Fiber optic networks are playing a crucial role in supporting smart city solutions, Internet of Things (IoT) devices, and other digital innovations as part of various government initiatives. Asia Pacific region has emerged as a leading market due to its abundance of vendors developing robust and innovative machine learning solutions. With the Banking, Financial Services, and Insurance (BFSI) industry in the region expected to see significant growth in deploying security services, major companies are targeting this area to expand their operations. Additionally, Asian countries are at the forefront of advanced technologies and trends such as autonomous driving, artificial intelligence, e-health, and fintech. The region's digitalization landscape is diverse, with varying levels of readiness for capitalization, digital transformation, and regulatory capacities across different countries.

China automated machine learning market is expected to grow significantly over the forecast period. China is at the forefront of automated machine learning technology, with major technology companies like Alibaba, Tencent, and Baidu heavily investing in this field. The massive scale of the machine learning industry in China is evident from companies like Bytedance, whose popular app Toutiao has over 170 million daily active users consuming content generated by automated machine learning algorithms for personalized recommendations.

The automated machine learning market in India is expected to grow substantially over the forecast period. India has witnessed a rise in domestic manufacturing of fiber optic cables and related components such as automated machine learnings. The government of India’s Make in India initiative aims to promote local manufacturing and reduce import dependence. Several Indian companies have established manufacturing facilities, contributing to the growth of the domestic fiber optic manufacturing sector.

Japan automated machine learning market is expected to grow significantly over the forecast period. Japan is emerging as a leading adopter of automated machine learning (AutoML) technology, driven by factors such as an aging population, labor shortages, and the need for increased efficiency and innovation across various industries. Major Japanese companies like Panasonic, Sony, and Recruit are embracing AutoML to drive innovation, improve customer experience, and accelerate data science processes. Panasonic is making bold moves to adopt AutoML, while Sony has open-sourced its deep learning library to adapt to the urgency for AI adoption. Recruit, a $13 Million company, is leveraging AutoML to enhance customer experience.

Middle East & Africa (MEA) Automated Machine Learning Market Trends

With the significant growth of the oil & gas industry in several countries of the MEA region, the demand for robust communication infrastructure is increasing to support operations in challenging environments. This factor is propelling the need for automated machine learnings across the oil & gas industry in MEA. Several countries in the MEA region are actively pursuing digital transformation initiatives across various sectors, including government, healthcare, and finance. Investments in technologies such as Artificial Intelligence (AI), blockchain, and the Internet of Things (IoT) are gaining momentum, and subsequently instigating the initiatives for fiber optic infrastructure developments.

Kingdom of Saudi Arabia (KSA) automated machine learning market is expected to grow significantly over the forecast period. The Kingdom of Saudi Arabia is witnessing a surge in the adoption of machine learning and artificial intelligence technologies across various sectors, including healthcare, finance, education, and telecommunications. The government has launched initiatives like the National Program for Information Technology Development (NPITD) to train 1,000 skilled engineers in AI and machine learning, in partnership with the Saudi Data and AI Authority (SDAIA) and King Abdullah University of Science and Technology.

The automated machine learning market in the United Arab Emirates (UAE) is expected to grow substantially over the forecast period. The UAE government recognizes the importance of AutoML and is taking initiatives to foster its development and adoption. The National Program for Information Technology Development (NPITD) has revealed its initiative to educate 1,000 proficient engineers in AI and machine learning. This effort is in collaboration with the Saudi Data and AI Authority (SDAIA) and King Abdullah University of Science and Technology.

Key Automated Machine Learning Company Insights

Prominent firms have used product launches and developments, followed by expansions, mergers and acquisitions, contracts, agreements, partnerships, and collaborations as their primary business strategy to increase their market share. The companies have used various techniques to enhance market penetration and boost their position in the competitive industry. For instance, in May 2024, During the annual IBM Think conference, IBM revealed its collaboration with Amazon Web Services (AWS) to enable the complete array of IBM solutions within the data platform and WatsonX AI to be utilized alongside AWS services. The partnership aims to merge IBM WatsonX.Governance with Amazon SageMaker-a service designed to develop, train, and deploy machine learning (ML) and generative AI models using fully managed infrastructure, tools, and workflows. This joint effort facilitates Amazon SageMaker and WatsonX customers in managing model risks and fulfilling compliance requirements associated with recent regulations like the EU AI Act. This integration complements the existing availability of the WatsonX platform in the AWS Marketplace, which includes IBM WatsonX.AI and WatsonX.Data as customer-managed offerings.

Recent Developments

• In May 2024, Leveraging over two decades of collaboration, IBM and Adobe are providing clients with the expertise and technology to fully utilize Generative AI in marketing, content creation, and brand management. This is accomplished through a distinctive partnership that encompasses both technology solutions and consulting services, fostering collaborative innovation across hybrid cloud infrastructure, data utilization, applications, and a diverse Generative AI strategy.

• In April 2024, IBM on its WatsonX AI and data platform has launched Meta Llama 3, the latest iteration of Meta's open-source large language model. This extension enhances IBM's watsonx.ai model repository, facilitating enterprise innovation with its Granite series models, alongside offerings from top model providers such as Meta.

Automated Machine Learning Market Report Scope

Global Automated Machine Learning Market Report Segmentation

This report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2017 to 2030. For this study, Grand View Research has segmented the global automated machine learning market report based on offering, enterprise size, deployment, application, vertical, and region:

• Offering Outlook (Revenue, USD Million, 2017 - 2030)

  • Solution

  • Services

• Enterprise Size Outlook (Revenue, USD Million, 2017 - 2030)

  • SMEs

  • Large Enterprises

• Deployment Outlook (Revenue, USD Million, 2017 - 2030)

  • Cloud

  • On-premises

• Application Outlook (Revenue, USD Million, 2017 - 2030)

  • Data Processing

  • Feature Engineering

  • Model Selection

  • Hyperparameter Optimization Tuning

  • Model Ensembling

  • Others

• Vertical Outlook (Revenue, USD Million, 2017 - 2030)

  • BFSI

  • Retail & E-commerce

  • Healthcare

  • Government & Defense

  • Manufacturing

  • Media & Entertainment

  • Automotive & transportation

  • IT & Telecommunications

  • Others

• Regional Outlook (Revenue, USD Million, 2017 - 2030)

  • North America

    • U.S.

    • Canada

  • Europe

    • Germany

    • UK

    • France

  • Asia Pacific

    • China

    • Japan

    • India

    • South Korea

    • Australia

  • Latin America

    • Brazil

    • Mexico

  • Middle East and Africa (MEA)

    • Kingdom of Saudi Arabia

    • UAE

    • South Africa