(MENAFN- GlobeNewsWire - Nasdaq) The North America Automated Machine Learning (AutoML) Market is projected to grow from US$ 1.02 billion in 2024 to US$ 13 billion by 2033, with a CAGR of 32.66%. Key growth drivers include rising demand for AI-driven solutions, a shortage of skilled data scientists, burgeoning cloud adoption, and advancements in machine learning. AutoML simplifies complex tasks like data preparation and model selection, enabling faster decision-making and operational efficiency across sectors like healthcare, finance, and retail. Challenges include data privacy, integration complexity, and high implementation costs. Key players include DataRobot, Amazon AWS, IBM, and Microsoft.Dublin, Nov. 28, 2025 (GLOBE NEWSWIRE) -- The "North America Automated Machine Learning Market Report by Offering, Enterprise Size, Deployment Mode, Application, End Use, Countries and Company Analysis 2025-2033" report has been added to ResearchAndMarkets's offering.
The North America Automated Machine Learning Market is expected to reach US$ 13 billion by 2033 from US$ 1.02 billion in 2024, with a CAGR of 32.66% from 2025 to 2033.
The North American AutoML market is growing due to rising demand for AI-driven analytics, a shortage of skilled data scientists, increased adoption of cloud-based platforms, enterprise digital transformation, advances in machine learning algorithms, and the need for faster, more cost-effective data insights.
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The North American AutoML market is primarily driven by the increasing deployment of AI and machine learning in industries such as healthcare, banking, and IT. Enterprises confront a shortage of skilled data scientists, making automated ML solutions appealing for simplifying model creation. Cloud-based platforms and enterprise digital transformation programs drive up need for scalable, cost-effective solutions.
AutoML speeds up model training, deployment, and predictive analytics, enabling businesses to gain actionable insights faster. Furthermore, advances in algorithms, data preprocessing, and hyperparameter optimization increase AutoML efficiency, which drives adoption. Organizations use these platforms to cut operating expenses, make better decisions, and gain a competitive advantage.
Growth Drivers for the North America Automated Machine Learning Market
Rising AI and ML Adoption
The expanding use of artificial intelligence (AI) and machine learning (ML) in North American sectors is a significant driver of the Automated Machine Learning (AutoML) market. Organizations in healthcare, banking, retail, and IT are rapidly using AI/ML to get actionable insights, improve decision-making, and increase operational efficiency.
The scarcity of trained data scientists has fueled AutoML adoption, as these platforms automate complicated activities like model selection, hyperparameter tuning, and deployment. Oracle MySQL HeatWave, which was 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 the MySQL database, removing the need to transmit data or models to third-party solutions.
This automation makes adoption easier for businesses, shortens development time, and highlights how increased AI/ML usage directly drives demand for AutoML solutions in North America.
Cloud-Based Platform Integration
Cloud-based platform integration is a key driver in the North American AutoML market. Enterprises are increasingly relying on cloud infrastructure to provide scalable, cost-effective data storage and computational resources that support AutoML platforms. Cloud connection provides easy access to massive datasets, real-time analytics, and collaborative model building, allowing enterprises to deploy machine learning solutions more quickly. SaaS-based AutoML solutions reduce the need for costly on-premises infrastructure, decreasing the entry barrier for small and medium-sized businesses.
Cloud systems also offer multi-region operations, security compliance, and easy scalability, all of which are crucial in industries like healthcare, banking, and e-commerce. Organizations that integrate AutoML with cloud services can automate data pretreatment, model training, and deployment while minimizing operational complexity. This collaboration between cloud computing and AutoML accelerates adoption, improves efficiency, and fosters market growth throughout North America.
Algorithm and Technology Advancements
The North American AutoML market is being driven by advances in algorithms and machine learning technologies. Continuous progress in feature engineering, hyperparameter optimization, neural architecture search, and model selection enables AutoML platforms to produce extremely accurate and efficient models with minimal human interaction. The incorporation of AI explainability, anomaly detection, and reinforcement learning improves AutoML capabilities.
These technical advancements lessen reliance on expert data scientists while speeding up 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 healthcare diagnostics, financial forecasting, and predictive maintenance. As AutoML grows more complex and powerful, it expands its usage across industries, fuelling the expansion of the North American industry.
Challenges in the North America Automated Machine Learning Market
Data Privacy and Security Concerns
Data privacy and security remain significant problems for the North American AutoML business. AutoML platforms require access to enormous amounts of sensitive data, such as medical records, financial transactions, and consumer information. Compliance with requirements like as HIPAA, CCPA, and GDPR is required, but maintaining security across cloud-based or multi-tenant AutoML systems can be challenging.
Unauthorized access, data breaches, or exploitation of sensitive datasets can result in legal consequences, reputational damage, and a loss of client trust. To protect data, enterprises must make significant investments in encryption, access controls, and monitoring technologies. These obstacles hamper adoption, especially for small and medium-sized businesses that lack dedicated security infrastructure, even as demand for AutoML solutions grows.
Integration Complexity
The complexity of integration is a major barrier to AutoML adoption in North America. Enterprises frequently deal with old IT systems, various databases, and heterogeneous applications that must work together smoothly with AutoML platforms. Aligning these systems necessitates extensive technical knowledge, modification, and time, as well as compatibility with existing analytics, ERP, and cloud infrastructure.
Failure to integrate effectively might result in fragmented data, lower model accuracy, and inefficiencies in predictive analytics workflows. Furthermore, enterprises must verify that AutoML outputs are compatible with organizational decision-making pipelines and operational procedures. These integration barriers can hinder acceptance, raise expenses, and limit the efficient use of AutoML, especially in firms with complicated IT environments or limited in-house technical personnel.
Recent Developments in North America Automated Machine Learning Market
June 2025: Oracle committed USD 40 billion to purchase Nvidia GPUs for the OpenAI-backed Stargate data centre in Texas, scheduled to go live in 2026. June 2025: AWS unveiled Project Rainier, deploying hundreds of thousands of Trainium 2 chips across US sites to quintuple available AI-training capacity.
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