Automated Machine Learning Solution Market – Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Offering (Platform and Service), By Deployment (On-Premise and Cloud), By Automation Type (Data Processing, Feature Engineering, Modeling, and Visualization), By Enterprise Size (Large Enterprises and SMEs), By End-Users (BFSI, Retail and E-Commerce, Healthcare, and Manufacturing), By Region and Competition 2021-2031F

Forecast Period	2027-2031
Market Size (2025)	USD 3.25 Billion
CAGR (2026-2031)	42.48%
Fastest Growing Segment	Manufacturing
Largest Market	North America
Market Size (2031)	USD 27.19 Billion

Market Overview

The Global Automated Machine Learning Solution Market will grow from USD 3.25 Billion in 2025 to USD 27.19 Billion by 2031 at a 42.48% CAGR. Automated Machine Learning (AutoML) solutions are comprehensive software platforms designed to automate the end-to-end process of applying machine learning to real-world problems, streamlining tasks ranging from data preprocessing and feature engineering to model selection and hyperparameter tuning. The market’s growth is primarily supported by the democratization of data science, which allows business professionals with limited coding expertise to develop predictive models, and the critical shortage of skilled data scientists that necessitates more efficient resource utilization. According to CompTIA, in 2024, 43% of channel companies planned to sell AI-related software and services, reflecting a significant supply-side shift to meet the burgeoning organizational demand for accessible and scalable artificial intelligence tools.

However, a substantial challenge impeding the universal expansion of the market is the lack of transparency and explainability in automated models, frequently referred to as the "black box" problem. In highly regulated sectors such as finance and healthcare, the inability to interpret how a model derives its specific predictions creates compliance risks and erodes stakeholder trust. This opacity, combined with stringent data privacy requirements and the complexity of integrating these autonomous systems into established legacy infrastructures, continues to create friction for risk-averse enterprises aiming to deploy these solutions at scale.

Key Market Drivers

The critical shortage of skilled AI professionals acts as a primary catalyst for the widespread adoption of automated machine learning solutions. As organizations strive to integrate artificial intelligence into their core operations, the scarcity of qualified data scientists creates a substantial bottleneck that necessitates the use of platforms capable of reducing technical barriers. By automating complex processes such as feature selection and hyperparameter tuning, these tools enable enterprises to mitigate the talent gap and maintain competitive momentum without requiring an extensive team of specialized experts. According to IBM, August 2025, in the 'The 5 biggest AI adoption challenges for 2025' report, 42% of respondents identified inadequate expertise as a significant challenge preventing organizations from effectively scaling their artificial intelligence initiatives.

Concurrently, the acceleration of model development cycles and the pursuit of operational efficiency drive the implementation of these autonomous systems. In a business environment where speed to market is essential, automated solutions significantly reduce the time required to move from raw data to actionable insights by eliminating repetitive manual coding tasks. This streamlined workflow allows technical teams to focus on high-level strategy rather than routine maintenance, thereby enhancing overall productivity and ensuring rapid deployment. According to Microsoft, May 2025, in the '2025 Work Trend Index Annual Report', 90% of AI power users stated that using AI makes their workload more manageable, underscoring the efficiency gains realized through intelligent automation. Furthermore, the broader financial commitment to these technologies highlights their strategic importance; according to Stanford HAI, April 2025, in the 'AI Index Report 2025', corporate AI investment reached $252.3 billion in 2024, reflecting the massive capital influx supporting this market's expansion.

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Key Market Challenges

The lack of transparency and explainability in automated models, frequently termed the "black box" problem, serves as a significant restraint on the Global Automated Machine Learning Solution Market. In highly regulated sectors such as finance and healthcare, the opacity of algorithmic decision-making directly conflicts with the need for accountability and interpretability. Stakeholders must be able to validate how a model derives its predictions to satisfy stringent legal mandates, yet the autonomous nature of many AutoML platforms obscures this logic. This inability to audit decision pathways erodes trust among risk-averse enterprises, causing them to delay or limit the deployment of these tools in mission-critical operations where errors could lead to severe reputational and financial damage.

This friction is exacerbated by a widespread lack of organizational readiness to govern these complex systems effectively. According to ISACA, in 2024, only 15% of organizations had established formal AI policies, highlighting a critical governance gap that leaves many businesses unprepared to manage the compliance risks associated with opaque automated technologies. Without robust frameworks to ensure the ethical and transparent use of these "black box" models, enterprises remain hesitant to integrate AutoML solutions into established legacy infrastructures. Consequently, this governance deficiency slows market penetration in high-value industries that prioritize regulatory adherence over operational speed.

Key Market Trends

The integration of Generative AI for lifecycle automation is redefining the Global Automated Machine Learning Solution Market by shifting the focus from simple hyperparameter tuning to comprehensive code and data synthesis. Advanced generative models are now capable of autonomously authoring deployment scripts, generating synthetic training data, and creating technical documentation, acting as intelligent operational partners rather than passive tools. This evolution accelerates development timelines and mitigates the skills shortage by handling complex engineering tasks that previously required manual intervention. According to Google Cloud, November 2024, in the '2024 DORA Report', 76% of developers reported using AI-powered tools daily, reflecting the pervasive adoption of these automated capabilities to streamline core software and model development workflows.

Concurrently, the market is converging with MLOps frameworks to address the operational challenges created by the mass production of automated models. As organizations leverage AutoML to generate algorithms at an unprecedented pace, robust continuous management systems are becoming essential to monitor, govern, and retrain these assets effectively in dynamic production environments. This trend emphasizes the shift from model creation to sustainable lifecycle management, ensuring that the volume of deployed solutions does not overwhelm legacy infrastructure. According to Databricks, June 2024, in the 'State of Data + AI Report', the number of machine learning models managed by organizations grew by 11 times year-over-year, highlighting the critical need for scalable operational architectures to support this explosive growth in automated model deployment.

Segmental Insights

The Manufacturing segment is identified as the fastest-growing category in the Global Automated Machine Learning Solution Market, largely due to the pervasive adoption of Industry 4.0 principles. Organizations within this sector are prioritizing automated machine learning to refine predictive maintenance capabilities, thereby minimizing unplanned operational downtime. Concurrently, the demand for superior quality control and efficient supply chain management drives the integration of these technologies. By leveraging automated models to interpret extensive industrial data, manufacturers can significantly elevate production consistency and operational cost-efficiency, securing a competitive advantage in a data-centric industrial landscape.

Regional Insights

North America currently leads the Global Automated Machine Learning Solution Market, largely due to the high concentration of major technology corporations and widespread adoption within the banking and healthcare sectors. This dominance is reinforced by substantial investments in cloud infrastructure and a critical need to bridge the skills gap in data science through automation. Additionally, the National Institute of Standards and Technology (NIST) supports this growth by developing essential frameworks that standardize artificial intelligence deployment. These factors collectively establish a robust ecosystem, ensuring the region remains the central hub for market expansion.

Recent Developments

• In December 2024, Dataiku announced the launch of Dataiku Stories, a new generative AI-powered feature integrated into its Universal AI Platform. This solution allows business users to automatically generate visual presentations and actionable insights from their data using simple natural language prompts. By embedding this capability directly within the platform where data and models are managed, the tool ensures that the resulting narratives and slides are based on trusted, real-time enterprise information. This product launch aims to democratize access to advanced analytics, enabling teams to create accurate, dynamic data stories without requiring extensive coding skills or manual data updates.
• In November 2024, DataRobot launched a new suite of enterprise AI tools designed to accelerate the development and deployment of both generative and predictive AI applications. The release featured a comprehensive enterprise AI suite that includes pre-built templates for various use cases, such as agentic workflows and automated content creation systems. Furthermore, the company introduced advanced AI observability and compliance capabilities, including automated documentation and real-time intervention tools. These innovations aim to help organizations govern, monitor, and scale their AI initiatives effectively while ensuring reliability and adherence to evolving regulatory standards.
• In September 2024, Qlik unveiled significant enhancements to its automated machine learning capability, Qlik AutoML, to further streamline model development. The update introduced intelligent model optimization, a feature that automates the iteration process and applies data science best practices to ensure high model performance with minimal manual intervention. Additionally, the release included native machine learning analytics that provide auto-generated dashboards for analyzing, comparing, and explaining model predictions. These improvements were fully integrated into Qlik Cloud, offering analytics teams a comprehensive, code-free environment to build, deploy, and monitor predictive models for proactive decision-making.
• In June 2024, Snowflake announced a strategic collaboration with LandingAI to integrate advanced computer vision capabilities directly into the Snowflake Data Cloud. This partnership resulted in the launch of LandingLens as a Snowflake Native App, enabling users to create and deploy computer vision models using their proprietary data residing within the platform. The solution facilitates the automation of visual AI tasks by allowing customers to train and run models without the need for data movement. This integration empowers enterprises to easily adopt automated machine learning for image data, streamlining workflows for use cases such as manufacturing quality control and visual inspection.

Key Market Players

• Datarobot Inc.
• Amazon Web Services Inc.
• dotData Inc.
• International Business Machines Corporation
• Dataiku
• EdgeVerve Systems Limited
• Big Squid Inc.
• SAS Institute Inc.
• Microsoft Corporation
• Determined.ai Inc.

By Offering	By Deployment	By Automation Type	By Enterprise Size	By End-Users	By Region
Platform and Service	On-Premise and Cloud	Data Processing Feature Engineering Modeling and Visualization	Large Enterprises and SMEs	BFSI Retail and E-Commerce Healthcare and Manufacturing	North America Europe Asia Pacific South America Middle East & Africa

Report Scope:

In this report, the Global Automated Machine Learning Solution Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

• Automated Machine Learning Solution Market, By Offering:

• Platform and Service

• Automated Machine Learning Solution Market, By Deployment:

• On-Premise and Cloud

• Automated Machine Learning Solution Market, By Automation Type:

• Data Processing
• Feature Engineering
• Modeling
• and Visualization

• Automated Machine Learning Solution Market, By Enterprise Size:

• Large Enterprises and SMEs

• Automated Machine Learning Solution Market, By End-Users:

• BFSI
• Retail and E-Commerce
• Healthcare
• and Manufacturing

• Automated Machine Learning Solution Market, By Region:

• North America
• United States
• Canada
• Mexico
• Europe
• France
• United Kingdom
• Italy
• Germany
• Spain
• Asia Pacific
• China
• India
• Japan
• Australia
• South Korea
• South America
• Brazil
• Argentina
• Colombia
• Middle East & Africa
• South Africa
• Saudi Arabia
• UAE