Automated Machine Learning (AutoML) Market Size, Share, Growth, and Industry Analysis, By Type (Platform and Service), By Application (Large Enterprise and SMEs), and Regional Insights and Forecast to 2033

AUTOMATED MACHINE LEARNING (AUTOML) MARKET OVERVIEW

The global Automated Machine Learning (AutoML) market size is predicted to reach USD XX billion by 2033 from USD XX billion in 2025, registering a CAGR of XX% during the forecast period.

Automated Machine Learning (AutoML) market is moving sky high since Corporates are adopting to AI-enabled solutions for automated model development and deployment at a faster pace, as opposed in native ways. AutoML platforms provide unskilled users with the ability to model, train and fine-tune machine learning models with little to no intervention from a data scientists hands, simplifying data science projects and lessening time needed. The worldwide market for the OEM is driven by increasing adoption, particularly in various industries such as healthcare, finance, retail & manufacturing due the prevalent demand of automated AI based consumer-written search engine. Organizations are using AutoML to gain a competitive edge, make better decisions and provide the customers with superior experiences by having data at their fingertips.

Cloud AutoML is becoming popular as enterprise-scale AI capabilities are most commonly required in scalable and cost-efficient. Besides, the increasing incorporation in business intelligence / analytics platforms as well as market adoption is driven by the incorporation of AutoML in business intelligence solutions. The AutoML Market is expected to grow dramatically with help of deep learning advancements, natural language processing (NLP) and predictive analytics. Forthcoming investments in AI technology and the introduction of no-code/low-code AI solutions will also fuel market expansion, with AI becoming progressively available to businesses at every range.

COVID-19 IMPACT 

Automated Machine Learning (AutoML) Industry Had a Positive Effect Due to supply chain disruption during COVID-19 Pandemic

The global COVID-19 pandemic has been unprecedented and staggering, with the market experiencing higher-than-anticipated demand across all regions compared to pre-pandemic levels. The sudden market growth reflected by the rise in CAGR is attributable to the market’s growth and demand returning to pre-pandemic levels.

During the COVID 19 pandemic, Automated Machine Learning (AutoML) market got a good leg-up as enterprises sought to manage operational woes by relying on AI-driven automation. Dealing with significant changes in workforce availability and business disruptions, this forced organizations to use AutoML to speed up their decision-making from data and increase efficiency. Pretty soon, cloud AutoML solutions turned into hyper-demanded solutions as remote work was the new normal causing more industries (health care included) and organizations to adopt it. Beyond, the companies used AutoML for predicting analytics that helped predict pandemic risks and predict supply chain optimization as well as customer engagement strategies. Investments in AI and AutoML solutions were further accelerated by the greater emphasis on digital transformation pre & post pandemic. The rising importance of automation and AI-driven innovation by enterprises will continue to drive growth in the post-pandemic AutoML market.

LATEST TREND

Growing Adoption of Generative AI and No-Code Platforms Growing Fitness Recognition to Drive Market Growth

One of the main Automated Machine Learning (AutoML) market trends that are changing as we speak are the incorporation of generative AI and large language models (LLMs), for improvements in model training to off-the-shelf automation. Organizations are using AI to automate harder machine learning workflows, somewhat converging away from the need for deep technical talent. With this shifting landscape, AutoML becomes more available to the non-experts which will scale adoption in areas including healthcare, finance & retail and more. Low-code and no-code AutoML platforms are also enabling corporate citizen who know how to use Excel to deliver AI-driven solutions with no programming hassle. Enterprise applications are increasingly turning to cloud-based AutoML services that provide scalable, low cost solutions for rapid deployment of AI models. While businesses keep working on democratizing AI, AutoML is going to be a key enabler of innovation and operational efficiency in the coming years.

AUTOMATED MACHINE LEARNING (AUTOML) MARKET SEGMENTATION

By Type

Based on type, the global market can be categorized into platform and service

• Platform: platform has AI-enabled AutoML software & build tools that automate data preparation simplification, model search, hyper parameter tuning and deployment. Adoption is being driven by a proliferation of straightforward no-code/low-code ML solutions, making machine learning accessible to non-experts.

• Service: The service category includes consulting and training support related to integration AutoML to practices. We are seeing more and more companies seek out help from the feature service providers to have rapid deployment/management of AutoML based solutions powered by AI-driven automation.

By Application

Based on application, the global market can be categorized into large enterprise and SMEs

• Large Enterprise: AutoML is used at scale by large companies to evolve their data analytics, predictive modeling and decision-making processes Adoptable across industries like finance, health-care and retail for scalable AI solutions & cost-effective automation are the changing needs.

• SMEs: AutoML for Small and Medium enterprises (SMEs) aim to bridge the lack of in-house data science skills required for AI-driven insights. The availability of cloud-based AutoML solutions at competitive price points makes it easier for SMEs to integrate AI into their operation.

MARKET DYNAMICS

Market dynamics include driving and restraining factors, opportunities and challenges stating the market conditions.

Driving Factors

Rising Demand for No-Code and Low-Code AI Solutions to Boost the Market

Key factor driving Automated Machine Learning (AutoML) market growth is the higher requirement for easy AI and machine learning deploy The adoption of insights driven by AI in different sectors such as healthcare, finance, retail or manufacturing is forcing many businesses to start using AI in decisioning & optimisation. But the issue is there is lack of experienced data scientists and the traditional machine learning development process is very hard. AutoML platforms automate the heavy lifting (such as data preparation and feature engineering, model selection etc.) which less-experienced users can take advantage of, to move traditional ML from experts only to machine-learning for all. Moreover, the auto ML in cloud-based platforms broaden scalability as well reduces the barrier of that it can reach all organization size to use AI without spending lot of money infra. In the drive for more affordable implementations of AI, demand for no-code and low-code AML solutions is expected to ramp.

Growing Adoption of AI and Data-Driven Decision-Making to Expand the Market

The exponentially increasing digital transformation of industries, has a very strong adjacency to the soaring demand of AI-driven automation that is propelling growth in AutoML Market. This data is being generated by businesses in bulk, and extracting insights from this data for predictive analytics, customer behavior, identity theft and operational optimization is key to staying ahead in the competition. AutoML shortens the model development life-cycle so enterprises can gain value from their data faster without the need of such high levels of human intervention to manage the whole piece. Companies like e-commerce uses AutoML for personalized recommendations while healthcare organizations use it as predictive diagnostics and treatment planning. AutoML is also taken into use by financial institutions for better risk assessment and fraud prevention as well. This means that as the use of AI grows ever more widespread, we will see an increasing requirement for automated, scalable and fast machine learning solutions that are enforced by demand across sectors in AutoML.

Restraining Factor

High Implementation Costs and Integration Challenges to Potentially Impede Market Growth

This notwithstanding, the challenges of implementation and integration found in the AutoML market which is fast expanding make it unscalable as a barrier especially for small and medium enterprises (SMEs). AutoML platforms are customarily deployed at the cost of extensive cloud infrastructure, computing resources and data warehousing which is not affordable to budgeted companies. Also, integrating AutoML solutions with the current IT systems, Databases and workflows in organizations — albeit possible — can complicate and time-consuming without proper technical know-how that many organizations have. Legacy adoption problems lead to falling adoption rates and make it fairly hard for enterprises to take full advantage of AutoML in practice. To alleviate these difficulties vendors are now concentrating on creating cost-effective AutoML solutions that can be easily integrated with a broad range of users.

Opportunity

Expansion of AutoML in Edge Computing and IoT Applications To Create Opportunity for the Product in the Market

Edge computing and Internet of Things (IoT) devices are taking off, making AutoML market sky-rocket. The need for automated machine learning (AutoML) at the edge is rising, as industries begin producing petabytes (or more) of real-time data from connected devices. AutoML can expedite decisions in the critical use cases such as predictive maintenance and manufacturing, live fraud detection in finance and personalized healthcare diagnostics. Moreover, combining AutoML with edge AI lowers the latency, enhances security and reduce reliance on cloud computing it is a cost-effective solution for the enterprise. As organizations move towards real-time analytics and automation powered by AI, AutoML will grow at a massive rate into edge computing and IoT ecosystems as they strive for immediate results.

Challenge

Ensuring Model Interpretability and Compliance with Regulations Could Be a Potential Challenge for Consumers

Model interpretability and compliance with all government regulations Discussion by default in Machine Learning gets going as an opaque “black box” because AutoML does the building of models & optimization at scale which really users regulasy not understand how they are made. The absence of transparency causes a challenge in areas such as finance, healthcare and insurance where most countries require that explanation for AI-driven decisions be explainable. Moreover, data privacy laws like GDPR and CCPA hold AI accountable and AutoML vendors will have to provide explainable as well bias-free models as a solution to regulation demands. Solving the hurdle calls for XAI (explainable AI) mechanisms and AI AutoML frameworks that track best practices in trust enabling across industries.

AUTOMATED MACHINE LEARNING (AUTOML) MARKET REGIONAL INSIGHTS

North America

Currently, North America enjoys a preeminent AutoML market share due to the fast-paced evolution of technology, adoption of artificial intelligence (AI) everywhere and concrete investments in AI automation. Key industry participants, research organisations and a constellation of AutoML tech startups drive innovation in AutoML solutions. With the ever-increasing demand for data-centric decision in the finance, healthcare, retail and manufacturing and the use case list continue to grow, demand for automated machine learning is expected. On top of that, the increased democratization of AI enabled by AutoML platforms (no-code/low-code) is accelerating the trajectory for adoption across all business sizes. Positioned at the head of regional growth, the United States Automated Machine Learning (AutoML) market is strongly positioned with abundant investment in AI research and enterprise adoption of AutoML solutions. Companies are embedding AI-enabled automation into their systems for improved productivity, predictive analytics & business intelligence. Despite competitions demanding heavy-duty AutoML in areas like autonomous vehicles, cybersecurity and personalized marketing get more and more industrial powers. The U.S. regulatory framework is also maturing to figure out AI ethics and governance, which may help enable AutoML technologies responsibly.

Europe

Europe is large growth in Automated Machine Learning (AutoML) market share due to AI and digital transformation, and laws to improve ethics by which AI are used. Every governments in countries such as Germany, the UK or France now are supporting AI research and innovation to remain competitive for technology (especially as there are long roads of development). The financial is one of the biggest AutoML adopters, where AI-predictive models driven by financial/credit scoring and fraud detection. Furthermore, manufacturing, automotive and healthcare are just some industries that employing AutoMLs to streamline processes and drive value improvement, customer experience. The European AI Regulation would put AutoML in a good light, going along with responsible use; making machine learning applications transparent and people accountable.

Asia

AutoML market share, Asia-Pacific is a coming up with massive growth rate among the regions on the back of rapid digitalization and the increasing adoption of AI in China, Japan, India, South Korea etc. The robust e-commerce, fintech and smart city initiatives across the region are fueling the need for automation powered by AI. China has experienced the increasingAutoML deployment across industries due to government-backed AI development programs in progress and deep learning technology adoption. Japanese manufacturing has interest into robotics and automation driven by AI, opening up new avenues for growth. AutoML tools have seen a growing demand in data science and enterprise decision making for the expanding IT and analytics industry present in India. With AI getting embedded in verticals from healthcare education to cybersecurity, there is a considerable growth potential for the AutoML market in Asia Pacific.

KEY INDUSTRY PLAYERS

Key Industry Players Shaping the Market Through Innovation and Market Expansion

The major competitors for Automated Machine Learning (AutoML) market are focusing on innovation, strategic partnerships and AI democratization between others to increase accessibility and speed of use. Businesses are using AutoML platforms in no-code/low-code to auto onboard businesses (with no AI expertise) and utilize machine learning. Key players are enhancing their cloud-based AutoML solutions to seamlessly connect with legacy enterprise systems. Deep learning automation and explainable AI: Strategic partnerships with tech companies, universities and research institutes are pushing the frontiers of technical progress in this space. Moreover, companies are ramping up on making model understandable, interpretable and ethical AI deployment in conformity with changing regulatory frameworks.

List Of Top Automated Machine Learning (AutoML) Companies 

• Amazon Web Services Inc. (United States)
• DataRobot (United States)
• EdgeVerve Systems Limited (India)
• H2O.ai Inc. (United States)
• IBM (United States)
• JADBio - Gnosis DA S.A. (Greece)
• QlikTech International AB (Sweden)
• Auger (United States)
• Google (United States)
• Microsoft (United States)
• SAS Institute Inc. (United States)

KEY INDUSTRY DEVELOPMENTS

February 2024: DataRobot (United States) acquired Agnostig, a company known for its open-source distributed computing platform, Covalent. This strategic move aims to enhance DataRobot's capabilities in agentic AI application development by integrating advanced compute orchestration and optimization features. The acquisition addresses the challenges organizations face in managing AI applications across fragmented infrastructures, enabling more efficient and scalable AI solutions.

REPORT COVERAGE

The Automated Machine Learning (AutoML) Market Report provides an in-depth analysis of the evolving industry landscape, highlighting key factors driving market growth, challenges, and opportunities. It examines market segmentation based on type, application, and region, offering valuable insights into demand patterns across different sectors. The report delves into the competitive landscape, profiling major players and their strategic initiatives to enhance AutoML capabilities. Additionally, it explores how advancements in artificial intelligence, cloud computing, and big data analytics are accelerating the adoption of AutoML solutions in various industries, including healthcare, finance, and retail.

Furthermore, the report assesses the impact of global events such as COVID-19, which have influenced market dynamics through disruptions in supply chains, shifts in business priorities, and increased reliance on automation. It highlights key industry developments, mergers and acquisitions, and innovative product launches that shape market expansion. Additionally, the report provides growth forecasts, investment opportunities, and regulatory insights to help businesses and investors make informed decisions in the evolving AutoML ecosystem.