DUBLIN, Nov. 28, 2025 (Globe Newswire) — The “North American Automated Machine Learning Market Report (By Product, Company Size, Deployment Mode, Application, End-use, Country and Company Analysis 2025-2033)” report has been added. ResearchAndMarkets.com Offerings.
The North American automated machine learning market is expected to reach USD 13 billion by 2033, from USD 1.02 billion in 2024, at a CAGR of 32.66% from 2025 to 2033.
The North American AutoML market is growing due to increasing demand for AI-driven analytics, shortage of skilled data scientists, increasing adoption of cloud-based platforms, digital transformation of enterprises, advancements in machine learning algorithms, and the need for faster and cost-effective data insights.
The North American AutoML market is primarily driven by the increasing adoption of AI and machine learning in industries such as healthcare, banking, and IT. Companies are facing a shortage of skilled data scientists, making automated ML solutions that simplify model creation attractive. Cloud-based platforms and enterprise digital transformation programs are driving the need for scalable and cost-effective solutions.
AutoML accelerates model training, deployment, and predictive analytics, enabling businesses to gain actionable insights faster. Additionally, advances in algorithms, data preprocessing, and hyperparameter optimization will improve the efficiency of AutoML and accelerate its adoption. Organizations use these platforms to reduce operating costs, make better decisions, and gain competitive advantage.
North America Automated Machine Learning Market Growth Drivers
Increased adoption of AI and ML
The growing use of artificial intelligence (AI) and machine learning (ML) in the North American sector is a key driver of the automated machine learning (AutoML) market. Healthcare, banking, retail, and IT organizations are rapidly using AI/ML to gain actionable insights, improve decision-making, and increase operational efficiency.
The shortage of trained data scientists is driving the adoption of AutoML, as these platforms automate complex activities such as model selection, hyperparameter tuning, and deployment. Oracle MySQL HeatWave, introduced in March 2022, is a notable example of in-database machine learning. HeatWave ML automates the entire ML lifecycle by storing trained models in a MySQL database, eliminating the need to send data and models to third-party solutions.
This automation allows companies to ease deployment and reduce development time, and we see increased usage of AI/ML directly driving demand for AutoML solutions in North America.
Cloud-based platform integration
Cloud-based platform integration is a key driver of the North American AutoML market. Enterprises are increasingly relying on cloud infrastructure to provide scalable and cost-effective data storage and compute resources to support AutoML platforms. Cloud connectivity provides easy access to large datasets, real-time analysis, and collaborative model building, allowing businesses to deploy machine learning solutions faster. SaaS-based AutoML solutions reduce the need for expensive on-premises infrastructure, lowering the barrier to entry for small and medium-sized businesses.
Cloud systems also offer multi-region operations, security compliance, and easy scalability. All of these are important in industries such as healthcare, banking, and e-commerce. Organizations that integrate AutoML with cloud services can automate data preprocessing, model training, and deployment while minimizing operational complexity. This collaboration between cloud computing and AutoML will accelerate adoption, improve efficiency, and drive market growth across North America.
Advances in algorithms and technology
The North American AutoML market is driven by advances in algorithms and machine learning technology. Continuous advances in feature engineering, hyperparameter optimization, neural architecture search, and model selection enable AutoML platforms to generate highly accurate and efficient models with minimal human intervention. Incorporating AI explainability, anomaly detection, and reinforcement learning improves AutoML’s capabilities.
These technological advances reduce reliance on specialized data scientists and shorten the ML lifecycle from data ingestion to deployment. Companies such as Google, Microsoft, and Oracle are incorporating these breakthroughs into their platforms to provide enterprise-ready solutions that improve predictive accuracy and operational efficiency. The use of cutting-edge algorithms enables new applications in medical diagnostics, financial forecasting, and predictive maintenance. As AutoML becomes more complex and powerful, its use will expand across industries and drive the expansion of the North American industry.
North America Automated Machine Learning Market Challenges
Data privacy and security concerns
Data privacy and security remain critical issues for AutoML businesses in North America. AutoML platforms require access to vast amounts of sensitive data such as medical records, financial transactions, and consumer information. Compliance with requirements such as HIPAA, CCPA, and GDPR is required, but maintaining security across cloud-based or multi-tenant AutoML systems can be difficult.
Unauthorized access, data breaches, and misuse of sensitive data sets can lead to legal liability, reputational damage, and loss of client trust. To protect data, companies must make significant investments in encryption, access control, and monitoring technologies. These obstacles hinder adoption, especially for small and medium-sized enterprises without dedicated security infrastructure, despite the growing demand for AutoML solutions.
Integration complexity
Integration complexity is a major barrier to AutoML adoption in North America. Enterprises often deal with legacy IT systems, disparate databases, and disparate applications that need to work smoothly with AutoML platforms. Tuning these systems requires extensive technical knowledge, modification, and time, as well as compatibility with existing analytics, ERP, and cloud infrastructure.
Failure to effectively integrate can lead to data fragmentation, reduced model accuracy, and inefficient predictive analytics workflows. Additionally, companies must validate that AutoML output is compatible with their organization’s decision-making pipelines and operational procedures. These integration barriers can hinder adoption, increase expenses, and limit the efficient use of AutoML, especially in companies with complex IT environments or limited in-house technical resources.
Recent trends in the North American automated machine learning market
- June 2025: Oracle pledges $40 billion to purchase Nvidia GPUs for its OpenAI-backed Stargate data center in Texas, scheduled to go online in 2026.
- June 2025: AWS announces Project Rainier, deploying hundreds of thousands of Trainium 2 chips across its U.S. sites, quintupling available AI training capacity.
Key attributes:
| report attributes | detail |
| Number of pages | 200 |
| Forecast period | 2024-2033 |
| Estimated market value in 2024 (USD) | 1.02 billion dollars |
| Projected market value to 2033 (USD) | $13 billion |
| compound annual growth rate | 32.6% |
| Target area | North America |
Analysis of major companies
- Data Robot Co., Ltd.
- Amazon Web Services, Inc.
- Dot Data Co., Ltd.
- IBM Co., Ltd.
- Dataik
- SAS Institute Inc.
- Microsoft Corporation
- Google LLC (Alphabet Inc.)
- H2O.ai
- ABLE Co., Ltd.
North America Automated Machine Learning Market Segment:
offering
Company size
Deployment mode
application
- data processing
- model ensemble
- Feature engineering
- Hyperparameter optimization tuning
- Model selection
- others
End use
- health care
- retail
- IT and communications
- Banking, financial services and insurance
- cars and transportation
- advertising and media
- manufacturing industry
- others
country
For more information on this report, please visit https://www.researchandmarkets.com/r/s87wcd.
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North America automated machine learning market
