Automated Machine Learning (AutoML)

Global Automated Machine Learning (AutoML) Market to Reach US$10.9 Billion by 2030

The global market for Automated Machine Learning (AutoML) estimated at US$1.5 Billion in the year 2024, is expected to reach US$10.9 Billion by 2030, growing at a CAGR of 38.8% over the analysis period 2024-2030. Solutions, one of the segments analyzed in the report, is expected to record a 34.7% CAGR and reach US$5.6 Billion by the end of the analysis period. Growth in the Services segment is estimated at 44.3% CAGR over the analysis period.

The U.S. Market is Estimated at US$428.6 Million While China is Forecast to Grow at 36.2% CAGR

The Automated Machine Learning (AutoML) market in the U.S. is estimated at US$428.6 Million in the year 2024. China, the world`s second largest economy, is forecast to reach a projected market size of US$1.5 Billion by the year 2030 trailing a CAGR of 36.2% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 34.0% and 33.0% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 25.3% CAGR.

Global Automated Machine Learning (AutoML) Market – Key Trends & Drivers Summarized

How AutoML Is Democratizing Machine Learning Across Industries

Automated Machine Learning (AutoML) is redefining the landscape of data science by enabling non-experts to build powerful machine learning (ML) models without requiring deep knowledge of algorithms, coding, or data preprocessing. At its core, AutoML automates the end-to-end process of applying ML to real-world problems—handling tasks like data cleaning, feature selection, model selection, and hyperparameter tuning. This growing sophistication is eliminating technical barriers and bringing advanced analytics capabilities to business professionals, product managers, and domain experts. As a result, demand for AutoML platforms is rising across sectors such as finance, retail, healthcare, manufacturing, and telecom, where time-to-insight and operational efficiency are mission-critical.

One of the strongest market trends is the convergence of AutoML with cloud-native platforms, which enables scalable, on-demand processing power and seamless model deployment. Vendors are now offering low-code or no-code AutoML interfaces integrated with popular business intelligence (BI) and data visualization tools. This trend is empowering business units to build custom predictive models using their own data, reducing dependency on data science teams and significantly accelerating decision cycles. At the same time, enterprise demand for explainable AI (XAI) is growing, and AutoML providers are responding with transparency and interpretability features that meet regulatory and audit requirements. This alignment with governance and compliance frameworks is further supporting AutoML’s enterprise-wide adoption.

Is AutoML Closing the Gap Between Data Science and Business?

AutoML is rapidly becoming a bridge between business goals and machine learning capabilities. Traditional ML workflows often require teams of skilled data scientists, engineers, and statisticians, which not all organizations can afford or scale. AutoML automates many of these labor-intensive tasks, reducing time and resources needed to train and deploy high-performance models. This democratization of ML allows organizations to extract value from their data faster and more efficiently, particularly in environments where business agility is a competitive advantage. From customer churn prediction to inventory forecasting and fraud detection, AutoML is being deployed to generate accurate, actionable insights with minimal manual intervention.

In addition to speeding up development cycles, AutoML enhances model consistency and reduces human error in model selection and parameter tuning. Many platforms include built-in optimization frameworks that iteratively test combinations of algorithms and parameters to arrive at the best-performing solution based on predefined objectives. This kind of automation not only saves time but also ensures that models are optimized to a degree often difficult to achieve manually. Moreover, real-time model monitoring and retraining capabilities are being integrated into AutoML platforms, allowing users to continuously refine model performance and adapt to changing data trends. By embedding these tools into business workflows, companies are closing the gap between data strategy and execution.

Where Is AutoML Making the Greatest Impact Across Sectors?

The adoption of AutoML is expanding rapidly across industries that rely heavily on data for competitive advantage. In financial services, institutions are using AutoML to model credit risk, detect fraudulent transactions, and optimize investment strategies—tasks that traditionally demanded highly specialized quant teams. In healthcare, AutoML is enabling more accurate diagnosis predictions, patient outcome modeling, and operational efficiency forecasting in hospitals and clinics. The retail sector is leveraging AutoML for dynamic pricing, personalized recommendations, and inventory demand forecasting, all of which are critical to customer satisfaction and profit margins.

Manufacturing and logistics companies are also benefiting from AutoML’s ability to improve supply chain visibility and predictive maintenance. By continuously analyzing sensor data, AutoML systems can forecast equipment failures, reduce downtime, and extend asset lifecycles. In telecommunications, AutoML is optimizing network performance, automating customer service insights, and predicting subscriber churn with high precision. Governments and public sector agencies are tapping into AutoML to improve policy design, manage social services more efficiently, and enhance public safety through crime trend analysis. As AutoML platforms become more accessible and industry-specific, their impact will deepen, transforming not just analytics workflows but also the way strategic decisions are made.

What’s Fueling the Growth in the AutoML Market?

The growth in the AutoML market is driven by several factors that are rooted in both technological advancement and increasing enterprise expectations. One of the strongest drivers is the acute global shortage of data science talent, which has created a compelling need for tools that simplify and automate complex ML processes. AutoML platforms are enabling business analysts and subject matter experts to build predictive models without having to master coding or advanced statistics. The rise of cloud computing has further fueled adoption, with scalable infrastructure allowing organizations to run multiple model iterations in parallel and deploy them instantly into production environments.

Another key driver is the growing emphasis on time-to-value. In today’s competitive business environment, speed is critical—and AutoML drastically reduces the time needed to move from raw data to production-ready insights. The increasing integration of AutoML into data platforms, customer relationship management (CRM) systems, and enterprise resource planning (ERP) tools is also boosting its relevance. Additionally, AutoML’s alignment with regulatory and compliance standards—thanks to features like model explainability and version tracking—is making it attractive in tightly regulated industries such as finance, insurance, and healthcare. Finally, the proliferation of use-case-specific AutoML models, which are pre-trained for particular business problems, is lowering adoption barriers and driving expansion across small to mid-sized enterprises as well as large corporations. These combined forces are shaping a robust, fast-growing market poised to redefine the future of machine learning deployment.

SCOPE OF STUDY:

The report analyzes the Automated Machine Learning (AutoML) market in terms of units by the following Segments, and Geographic Regions/Countries:

Segments:
Component (Solutions, Services); Application (Data Processing, Feature Engineering, Model Selection, Model Ensembling, Other Applications); Vertical (BFSI, Retail & eCommerce, Healthcare & Life Sciences, IT & ITeS, Telecommunications, Manufacturing, Automotive & Transportation, Other Verticals)

Geographic Regions/Countries:
World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; and Rest of Europe); Asia-Pacific; Rest of World.

Select Competitors (Total 23 Featured) -

• Alteryx, Inc.
• Amazon Web Services, Inc.
• Databricks, Inc.
• Dataiku SAS
• DataRobot, Inc.
• dotData
• Google Cloud Platform
• IBM Corporation
• Microsoft Learn
• The MathWorks, Inc.

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