Automated Machine Learning Solutions - Global Strategic Business Report

The global market for Automated Machine Learning Solutions was estimated at US$2.2 Billion in 2024 and is projected to reach US$17.5 Billion by 2030, growing at a CAGR of 41.2% from 2024 to 2030. This comprehensive report provides an in-depth analysis of market trends, drivers, and forecasts, helping you make informed business decisions. The report includes the most recent global tariff developments and how they impact the Automated Machine Learning Solutions market.

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.

Key Insights:

• Market Growth: Understand the significant growth trajectory of the Platform Offering segment, which is expected to reach US$13.4 Billion by 2030 with a CAGR of a 44.0%. The Service Offering segment is also set to grow at 33.8% CAGR over the analysis period.
• Regional Analysis: Gain insights into the U.S. market, valued at $601.0 Million in 2024, and China, forecasted to grow at an impressive 50.6% CAGR to reach $4.3 Billion by 2030. Discover growth trends in other key regions, including Japan, Canada, Germany, and the Asia-Pacific.

Why You Should Buy This Report:

• Detailed Market Analysis: Access a thorough analysis of the Global Automated Machine Learning Solutions Market, covering all major geographic regions and market segments.
• Competitive Insights: Get an overview of the competitive landscape, including the market presence of major players across different geographies.
• Future Trends and Drivers: Understand the key trends and drivers shaping the future of the Global Automated Machine Learning Solutions Market.
• Actionable Insights: Benefit from actionable insights that can help you identify new revenue opportunities and make strategic business decisions.

Key Questions Answered:

• How is the Global Automated Machine Learning Solutions Market expected to evolve by 2030?
• What are the main drivers and restraints affecting the market?
• Which market segments will grow the most over the forecast period?
• How will market shares for different regions and segments change by 2030?
• Who are the leading players in the market, and what are their prospects?

Report Features:

• Comprehensive Market Data: Independent analysis of annual sales and market forecasts in US$ Million from 2024 to 2030.
• In-Depth Regional Analysis: Detailed insights into key markets, including the U.S., China, Japan, Canada, Europe, Asia-Pacific, Latin America, Middle East, and Africa.
• Company Profiles: Coverage of players such as Aible, Akkio, Alteryx, Altair Engineering, and more.
• Complimentary Updates: Receive free report updates for one year to keep you informed of the latest market developments.

Some of the 34 companies featured in this Automated Machine Learning Solutions market report include:

• 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

This edition integrates the latest global trade and economic shifts as of June 2025 into comprehensive market analysis. Key updates include:

• Tariff and Trade Impact: Insights into global tariff negotiations across 180+ countries, with analysis of supply chain turbulence, sourcing disruptions, and geographic realignment. Special focus on 2025 as a pivotal year for trade tensions, including updated perspectives on the Trump-era tariffs.
• Adjusted Forecasts and Analytics: Revised global and regional market forecasts through 2030, incorporating tariff effects, economic uncertainty, and structural changes in globalization. Includes segmentation by product, technology, type, material, distribution channel, application, and end-use, with historical analysis since 2015.
• Strategic Market Dynamics: Evaluation of revised market prospects, regional outlooks, and key economic indicators such as population and urbanization trends.
• Innovation & Technology Trends: Latest developments in product and process innovation, emerging technologies, and key industry drivers shaping the competitive landscape.
• Competitive Intelligence: Updated global market share estimates for 2025, competitive positioning of major players (Strong/Active/Niche/Trivial), and refined focus on leading global brands and core players.
• Expert Insight & Commentary: Strategic analysis from economists, trade experts, and domain specialists to contextualize market shifts and identify emerging opportunities.
• Complimentary Update: Buyers receive a free July 2025 update with finalized tariff impacts, new trade agreement effects, revised projections, and expanded country-level coverage.