Automated Machine Learning Solutions

Global Automated Machine Learning Solutions Market to Reach US$17.5 Billion by 2030

The global market for Automated Machine Learning Solutions estimated at US$2.2 Billion in the year 2024, is expected to reach US$17.5 Billion by 2030, growing at a CAGR of 41.2% over the analysis period 2024-2030. Platform Offering, one of the segments analyzed in the report, is expected to record a 44.0% CAGR and reach US$13.4 Billion by the end of the analysis period. Growth in the Service Offering segment is estimated at 33.8% CAGR over the analysis period.

The U.S. Market is Estimated at US$601.0 Million While China is Forecast to Grow at 50.6% CAGR

The Automated Machine Learning Solutions market in the U.S. is estimated at US$601.0 Million in the year 2024. China, the world`s second largest economy, is forecast to reach a projected market size of US$4.3 Billion by the year 2030 trailing a CAGR of 50.6% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 34.3% and 38.7% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 36.4% CAGR.

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

Why Is AutoML Becoming the Cornerstone of Scalable AI Deployment Across Industries?
Automated Machine Learning (AutoML) solutions are rapidly revolutionizing the artificial intelligence (AI) landscape by enabling organizations to leverage machine learning models without deep technical expertise. Traditionally, building machine learning models required extensive knowledge of algorithms, data preprocessing, feature engineering, model selection, tuning, and deployment—all of which were resource-intensive and time-consuming. AutoML addresses these challenges by automating the entire pipeline, from data preparation to model optimization, making AI accessible to a broader range of users including business analysts, domain experts, and decision-makers. This democratization of AI is proving to be a significant value addition in sectors such as finance, healthcare, retail, manufacturing, and logistics, where organizations are seeking fast, cost-effective solutions to process vast datasets and extract actionable insights. With digital transformation accelerating across the globe, AutoML platforms such as Google Cloud AutoML, H2O.ai, DataRobot, and Amazon SageMaker Autopilot are witnessing widespread adoption. They are not only cutting down time-to-insight but also enhancing model accuracy and scalability, giving enterprises a competitive edge in real-time decision-making.

How Are Enterprise AI Strategies and Data Science Bottlenecks Fueling Demand?
The rising complexity and volume of enterprise data have created an urgent need for more efficient and scalable AI workflows. Many organizations are facing an acute shortage of skilled data scientists, which hampers their ability to fully harness the potential of machine learning. AutoML solutions are bridging this gap by providing user-friendly interfaces and pre-built algorithms that allow non-experts to build high-performing models. This shift is enabling cross-functional teams to collaborate on AI initiatives, driving innovation at scale. In addition, businesses are increasingly embedding AI into their core workflows—such as customer segmentation, fraud detection, supply chain optimization, and predictive maintenance—where speed and accuracy are paramount. AutoML fits perfectly into this landscape by offering faster iterations and the ability to deploy models seamlessly into production environments. The emergence of multi-cloud strategies and hybrid IT infrastructures has further boosted the demand for AutoML platforms that can integrate with existing data ecosystems and enterprise applications. These solutions are also evolving to support explainability, compliance, and governance—critical requirements in regulated industries such as healthcare and finance—making them even more indispensable.

Can AutoML Power the Next Wave of Industry-Specific AI Innovation?
AutoML is increasingly being tailored to meet the unique requirements of specific industries, unlocking new opportunities for vertical innovation. In healthcare, for instance, AutoML is being used to analyze complex patient data for early disease prediction, risk scoring, and treatment personalization, without compromising data privacy. In retail, it enables personalized recommendation engines, demand forecasting, and inventory optimization with minimal technical overhead. In finance, AutoML tools are helping institutions detect anomalies, optimize portfolios, and improve credit scoring models with enhanced transparency. Moreover, manufacturers are integrating AutoML into Industrial Internet of Things (IIoT) platforms to perform real-time equipment monitoring and quality control. This domain-specific customization is being made possible by modular AutoML frameworks that allow developers to incorporate proprietary datasets, business logic, and domain rules into the model-building process. Educational institutions and governments are also adopting AutoML for policy modeling, resource allocation, and civic data analysis. The flexibility, adaptability, and scalability of AutoML platforms make them ideal candidates for widespread integration into diverse digital ecosystems, ensuring that the next phase of AI adoption is both inclusive and industry-relevant.

The Growth in the Automated Machine Learning Solutions Market Is Driven by Several Factors…
Multiple key dynamics are accelerating the growth of the AutoML market, with technology and business imperatives converging to create unprecedented demand. First, the explosion of data generated through IoT devices, cloud platforms, and digital transactions is pushing organizations to adopt tools that can rapidly make sense of this information—AutoML being at the forefront. Second, the critical shortage of AI talent, particularly in small and mid-sized enterprises, is compelling firms to adopt solutions that enable citizen data scientists to build and deploy machine learning models independently. Third, the integration of AutoML capabilities into mainstream business intelligence platforms like Microsoft Power BI and Tableau is lowering the entry barrier for AI adoption in business operations. Fourth, increasing reliance on real-time decision-making in sectors like e-commerce, fintech, and telecommunications is fueling demand for models that can be quickly retrained and redeployed, a core strength of AutoML. Fifth, advancements in natural language processing (NLP), computer vision, and time series forecasting are being rapidly incorporated into AutoML platforms, expanding their use cases and utility. Sixth, the growing emphasis on model interpretability and AI ethics is leading developers to incorporate transparent, auditable machine learning pipelines—strengthening trust and compliance. Together, these trends underscore a robust and multi-faceted expansion trajectory for the automated machine learning solutions market.

SCOPE OF STUDY:

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

Segments:
Offering (Platform Offering, Service Offering); Deployment (On-Premise Deployment, Cloud-based Deployment); Automation Type (Data Processing Automation, Feature Engineering Automation, Modeling Automation, Visualization Automation); End-Use (BFSI End-Use, Retail & E-Commerce End-Use, Healthcare End-Use, Manufacturing End-Use)

Geographic Regions/Countries:
World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; Spain; Russia; and Rest of Europe); Asia-Pacific (Australia; India; South Korea; and Rest of Asia-Pacific); Latin America (Argentina; Brazil; Mexico; and Rest of Latin America); Middle East (Iran; Israel; Saudi Arabia; United Arab Emirates; and Rest of Middle East); and Africa.

Select Competitors (Total 34 Featured) -

• Aible
• Akkio
• Alteryx
• Altair Engineering
• Amazon Web Services (AWS)
• Appier
• Baidu
• BigML
• Dataiku
• DataRobot
• Databricks
• dotData
• Google (Alphabet)
• H2O.ai
• IBM
• Microsoft
• Oracle
• Salesforce
• ServiceNow
• Teradata

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