SAN FRANCISCO, USA, January 8, 2026 (Globe Newswire) — Automatic machine learning (AutoML) has emerged as one of the most impactful innovations within the artificial intelligence ecosystem, reshaping the way organizations develop, deploy, and scale machine learning models. By automating complex and time-consuming stages of the machine learning lifecycle, AutoML significantly lowers technical barriers and enables faster adoption of data-driven decision-making across industries. The global automated machine learning market is exhibiting strong growth momentum as enterprises increasingly focus on efficiency, scalability, and speed.
The AutoML platform enables business analysts, software engineers, and even non-technical personnel to develop accurate predictive models without deep data science expertise. This technology reduces barriers to AI adoption, reduces time to insight, and improves operational efficiency. The Automated Machine Learning market has been evaluated as follows: 1,730.54 million USD in 2024 and is predicted to expand at a steady pace. CAGR from 2025 to 2032 is 45.90%reaching the estimated market size 35,532,350,000 people by 2032.
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market drivers
1. Rapid growth in data volume increases demand for AutoML solutions
The amount of data generated by public sector activities and connected technologies is rapidly expanding, creating an environment where manual processing is no longer possible and increasing the demand for automated solutions such as AutoML. In the United States, the government's official open data portal, Data.gov, currently hosts more than 381,000 datasets available to federal, state, local, and tribal agencies, reflecting broader public sector data publication and accessibility efforts. Meanwhile, Saudi Arabia's National Databank initiative will integrate more than 320 government systems into a unified data repository in 2024, aggregate more than 100 TB of government data, and publish thousands of official datasets through open data platforms to support analysis and innovation.
These government-sponsored data initiatives demonstrate that vast and continually growing datasets are being created, published, and used across the public sector ecosystem. The rapid growth of formal, structured, machine-readable data highlights the challenges organizations face in manually extracting insights from such large amounts of data. Automated machine learning (AutoML) can help address this challenge by automating data preprocessing, model selection, and optimization, allowing you to generate insights from these extensive data resources more quickly and efficiently.
2. Lack of data science talent
A global shortage of skilled data scientists is limiting the adoption of traditional machine learning workflows across organizations. AutoML addresses this challenge by enabling users with limited technical expertise to build accurate, production-ready models, greatly reducing dependence on scarce and costly AI talent.
3. The need for faster insights
Modern business environments require real-time or near real-time insights. AutoML accelerates model development and deployment, enabling organizations to respond to market changes, customer needs, and operational challenges faster than ever before.
4. Cloud implementation
Cloud-based AutoML solutions provide scalable infrastructure and managed services, reducing initial investment. Cloud platforms also simplify model deployment, monitoring, and updating, facilitating adoption across small, medium, and large enterprises.
5. Integration with business intelligence tools
The AutoML platform is increasingly integrated with enterprise analytics and BI tools, enabling seamless data flow from collection to actionable insights. This powers decision-making across marketing, sales, finance, operations, and other business functions.
Market segmentation
1. AutoML Market, By Solution:-
| solution | Product example | key player | Main technical services |
| Standalone/On-Premise AutoML | H2O.ai Driverless AI | H2O.ai | An enterprise-grade AutoML platform that can be deployed on-premises. Automate feature engineering, model tuning, interpretability, and deployment within a secure local infrastructure. |
| DataRobot Platform (on-premises option) | data robot | Comprehensive AutoML with options for hybrid or on-premises deployment. Supports the creation, governance, and lifecycle management of automated model pipelines for regulated industries. | |
| IBM Watson Studio (enterprise deployable) | IBM | We offer on-premises deployment capabilities for AutoML as part of Watson Studio, allowing enterprises to automate model building and governance while keeping data within the corporate network. | |
| Cloud-based AutoML solution | Google Cloud AutoML / Vertex AI | Fully managed AutoML service on Google Cloud for structured data, vision, and NLP. Integrated with BigQuery and scalable computing. | |
| AWS SageMaker Autopilot | Amazon Web Services (AWS) | Cloud-native services within SageMaker that automate your entire ML pipeline, from preprocessing to conditioning to deployment to AWS infrastructure. | |
| Automated ML in Azure | microsoft azure | Cloud-hosted AutoML within Azure Machine Learning automates model selection, tuning, and deployment with seamless integration to Azure services. |
2. AutoML Market, By Region:-
| region | Market value in 2024 (million USD) | Regional growth drivers | CAGR (2025–2032) |
| North America | 513.57 | Broad enterprise-grade AI implementations, combined with mature cloud infrastructure and advanced machine learning platforms, are rapidly accelerating large-scale AutoML adoption across organizations. | 32.3% |
| Europe | 411.18 | Strict data privacy regulations and the growing importance of responsible AI are driving demand for automated machine learning workflows that are compliant, transparent, and auditable. | 35.4% |
| Asia Pacific | 437.88 | Accelerating digital transformation and increasing AI integration across industries are driving demand for AutoML solutions that enable rapid deployment and operationalization of advanced analytics. | 42.6% |
| latin america | 185.51 | The growing adoption of cloud and analytics, combined with the need to enable machine learning across business functions despite limited availability of skilled professionals, is closing the gap in AI adoption in the region and accelerating the adoption of AutoML. | 39.4% |
| middle east and africa | 182.40 | Increased government/corporate digitization programs and AI adoption | 40.8% |
AutoML technology trends
1. Democratization of AI
AutoML makes machine learning accessible to a wide range of users. A drag-and-drop interface and intuitive workflow enable business analysts and non-technical staff to efficiently build predictive models, accelerating adoption within your organization.
2. Integration with MLOps
Modern AutoML solutions integrate with: MLOps framework Ensure that your models are production-ready, continuously monitored, and retrained as data changes. This combination increases reliability and reduces operational risk.
3. Advanced feature engineering
Automated feature engineering is becoming increasingly sophisticated to identify hidden patterns and transform raw data into highly predictive variables. This improves model performance while reducing manual intervention.
4. Cloud-native AutoML
The trend toward cloud-native AutoML enables seamless integration with other AI and analytics services, such as data warehouses, visualization platforms, and real-time analytics engines.
5. Open source and proprietary solutions
This market is characterized by a good balance of open source tools (such as Auto-sklearn and TPOT) and proprietary platforms (Google Cloud AutoML, Microsoft Azure AutoML, and Amazon SageMaker Autopilot). Businesses often select solutions based on scalability, integration, and support requirements.
market challenges
- Dependence on data quality: AutoML cannot fully compensate for the loss of data quality. Pretreatment is still important.
- Explainability: Automated models can be opaque, creating challenges for regulated industries that require transparency.
- Prejudice and ethics: If your training data contains bias, AutoML can perpetuate it and require governance and human oversight.
- Integration complexity: Integrating AutoML into existing IT and business processes may require technical expertise and workflow redesign.
major market players
- Google Cloud AutoML: We offer managed AutoML solutions for images, videos, text, and tabular data.
- Microsoft Azure AutoML: An enterprise platform that integrates with Azure cloud services.
- Amazon SageMaker Autopilot: Provides cloud-native AutoML with model monitoring and deployment capabilities.
- Data robot: We focus on scalable AutoML solutions for enterprise applications.
- H2O.ai: Open source and commercial AutoML solutions for predictive analytics and AI-powered insights.
- Databricks AutoML: Cloud-first AutoML integrated with big data analytics pipelines.
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