AutoML Market Analysis, Size, and Forecast 2025-2029: North America (US and Canada), Europe (France, Germany, Italy, and UK), APAC (China, India, Japan, and South Korea), and Rest of World (ROW)

AutoML Market Size 2025-2029

The automl market size is valued to increase by USD 13.53 billion, at a CAGR of 44.8% from 2024 to 2029. Increasing democratization of AI amid persistent data science talent shortage will drive the automl market.

Market Insights

• North America dominated the market and accounted for a 39% growth during the 2025-2029.
• By Type - Services segment was valued at USD 390.40 billion in 2023
• By Deployment - Cloud segment accounted for the largest market revenue share in 2023

Market Size & Forecast

• Market Opportunities: USD 1.00 million 
• Market Future Opportunities 2024: USD 13531.20 million
• CAGR from 2024 to 2029 : 44.8%

Market Summary

• The market is experiencing significant growth as the democratization of Artificial Intelligence (AI) continues to gain momentum, addressing the persistent talent shortage in data science. AutoML, or Automated Machine Learning, streamlines the machine learning model development process by automating feature engineering, model selection, and hyperparameter tuning. This approach is increasingly being adopted across industries for various use cases, such as supply chain optimization and regulatory compliance. A notable trend in the market is the fusion of predictive autoML with generative AI, enabling lifecycle automation. Predictive autoML models are used to make predictions based on historical data, while generative AI models can create new data, such as synthetic images or text.
• By combining these technologies, businesses can automate the entire machine learning workflow, from data preparation to model deployment. Despite its advantages, the adoption of AutoML faces challenges. One of the primary concerns is the lack of trust and inherent black box nature of complex models. As AI systems become more sophisticated, understanding their inner workings becomes increasingly difficult. Addressing these challenges requires ongoing research and development in explainability and transparency, ensuring that businesses can trust and effectively utilize AutoML for their operational efficiency and strategic initiatives.

What will be the size of the AutoML Market during the forecast period?

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• The market continues to evolve, offering cloud-based Machine Learning (ML) platforms that automate feature selection, statistical significance testing, and the algorithm selection process. Unsupervised learning techniques, such as clustering and anomaly detection, are increasingly popular for identifying patterns and reducing bias-variance tradeoffs. Model interpretability tools and robustness assessment methods ensure transparency and prevent underfitting and overfitting. Scalable ML infrastructure, including distributed training frameworks and GPU acceleration techniques, enable faster model selection and parameter tuning. Semi-supervised learning and deep learning frameworks improve model accuracy with limited labeled data. Regularization methods, such as L1 and L2 regularization, enhance model performance by reducing complexity.
• Reinforcement learning algorithms optimize model behavior based on feedback from the environment. Model selection criteria, such as cross-validation methods and error rate reduction, ensure the best model for a given use case. Model monitoring systems and active learning strategies maintain model accuracy and adapt to new data. By implementing these advanced techniques, organizations can make informed decisions on product strategy, budgeting, and compliance, achieving significant improvements in model performance and business outcomes. For instance, a company may reduce error rates by 20% through the adoption of an automated ML platform.

Unpacking the AutoML Market Landscape

In the realm of data-driven business intelligence, Automated Machine Learning (AutoML) has emerged as a game-changer, streamlining model development and deployment processes. AutoML platforms automate various stages of the machine learning workflow, including model selection, training, and hyperparameter tuning. Compared to traditional logistic regression models, AutoML platforms employ bias mitigation strategies and machine learning models to improve accuracy by up to 20%. Automated model selection, data augmentation methods, and feature engineering techniques enable businesses to identify optimal models for their specific use cases, leading to a 30% reduction in time-to-insight. Anomaly detection systems integrated into AutoML pipelines enhance compliance alignment by proactively identifying outliers and potential threats. Performance evaluation metrics and model versioning systems ensure continuous improvement and maintainability of models. AutoML platforms support a wide range of applications, from linear regression models and time series forecasting to neural network architectures and natural language processing. By automating complex machine learning tasks, businesses can focus on strategic initiatives and achieve significant ROI improvements.

Key Market Drivers Fueling Growth

Amidst the growing democratization of Artificial Intelligence technology, the persistent shortage of skilled data science talent serves as a significant market driver. This trend underscores the increasing demand for user-friendly AI solutions and the necessity for organizations to invest in upskilling their workforce or partnering with data science experts to remain competitive.

• The market is experiencing significant growth as businesses across various sectors grapple with the increasing demand for artificial intelligence (AI) and the scarcity of skilled data science talent. With digital transformation becoming a strategic priority, organizations are seeking new ways to gain a competitive edge through data analysis. AutoML platforms, which automate the machine learning model building process, offer a solution to this challenge by democratizing access to advanced analytical capabilities. According to recent studies, up to 60% of enterprises report difficulty in finding the skilled data science personnel needed to implement AI initiatives.
• AutoML platforms have shown to reduce model development time by up to 80%, enabling businesses to quickly respond to market trends and improve forecast accuracy by 15%. These platforms are revolutionizing industries, from healthcare to finance, by enabling non-experts to build and deploy machine learning models, thereby accelerating innovation and driving business growth.

Prevailing Industry Trends & Opportunities

The fusion of predictive autoML and generative AI lifecycle automation is an emerging market trend. This combination of technologies is set to revolutionize the way artificial intelligence systems are developed and deployed. 

• The market is undergoing significant transformation as traditional automated machine learning converges with generative artificial intelligence. While early AutoML platforms focused on automating predictive tasks using structured data, the latest trend involves automating the entire lifecycle of generative AI. Enterprises are increasingly exploring applications of foundation models and large language models (LLMs) in areas like advanced conversational agents, content generation, and code creation. However, adapting these models for specific business contexts poses unique challenges, necessitating expertise in model selection, unstructured data preparation, prompt engineering, fine-tuning, and rigorous evaluation for safety and accuracy. This shift represents a fundamental expansion of AutoML's scope, offering substantial business benefits such as increased efficiency and improved model performance.
• For instance, one organization reported a 25% reduction in development time and a 15% improvement in forecast accuracy after implementing an AutoML solution for generative tasks. Another enterprise achieved a 35% increase in customer engagement through the deployment of an advanced conversational agent powered by AutoML technology.

Significant Market Challenges

The lack of transparency and complex, opaque nature of advanced models poses a significant challenge to industry expansion. 

• The market is experiencing significant growth and transformation, driven by its ability to generate accurate predictive models at an unprecedented speed. AutoML's applications span various sectors, including finance, healthcare, and manufacturing, where data-driven insights are crucial for informed decision-making. For instance, in finance, AutoML models can forecast stock prices with remarkable accuracy, enabling traders to make profitable investments. In healthcare, these models can predict disease outbreaks or identify potential health risks based on patient data, leading to improved patient outcomes. However, a persistent challenge in the adoption of AutoML is the inherent opacity of the complex models it produces.
• Despite generating predictions with high accuracy, the intricate internal logic of these systems, particularly those based on deep neural networks or large ensembles of trees, can be difficult to interpret. This "black box" phenomenon creates a substantial barrier to trust, especially in high-stakes and heavily regulated industries. To address this issue, efforts are underway to develop explainable AutoML models that provide clear rationales behind their predictions, thereby enhancing trust and facilitating operationalization. For instance, a leading financial institution reported a 25% increase in model adoption after implementing an explainable AutoML solution, while a healthcare organization experienced a 20% improvement in forecast accuracy.

In-Depth Market Segmentation: AutoML Market

The automl industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

• Type
  • Services
  • Platforms
  • Software tools
• Deployment
  • Cloud
  • On-premises
• Application
  • Data processing
  • Model selection
  • Hyperparameter tuning
• Sector
  • BFSI
  • Retail and e-commerce
  • Manufacturing
  • Healthcare
• End-user
  • Large enterprises
  • SMEs
• Geography
  • North America
    • US
    • Canada
  • Europe
    • France
    • Germany
    • Italy
    • UK
  • APAC
    • China
    • India
    • Japan
    • South Korea
  • Rest of World (ROW)

By Type Insights

The services segment is estimated to witness significant growth during the forecast period.

The market is witnessing continuous evolution, driven by advancements in machine learning models and automated techniques. AutoML platforms automate various stages of the model development process, from data preprocessing and feature engineering to model training and hyperparameter optimization. Techniques such as bias mitigation strategies, data augmentation methods, and anomaly detection systems are integral to AutoML's success. Services, a critical sub-segment, account for a significant market share and are expanding rapidly. These services bridge the gap between technology and business value, providing essential human expertise to mainstream enterprises. Professional services, including consulting and advisory, help organizations develop AI strategies, identify use cases, and select suitable AutoML platforms.

Model training pipeline services ensure efficient model training and deployment, while performance evaluation metrics and model explainability methods enhance transparency and trust. The integration of advanced techniques like neural network architecture, ensemble learning methods, and reinforcement learning algorithms further enhances AutoML's capabilities. For instance, the use of transfer learning applications in computer vision techniques has led to a 20% increase in model accuracy for some organizations. The market's growth is fueled by the increasing adoption of AutoML platforms across industries and applications, from regression algorithms and clustering algorithms to natural language processing and time series forecasting.

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The Services segment was valued at USD 390.40 billion in 2019 and showed a gradual increase during the forecast period.

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Regional Analysis

North America is estimated to contribute 39% to the growth of the global market during the forecast period.Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The market is experiencing significant evolution, with North America leading the charge as the most mature and dynamic region. The United States and Canada, in particular, are at the forefront of this innovation, driven by their technological superiority, robust venture capital ecosystem, and the presence of major tech corporations. Notably, cloud hyperscalers like Amazon Web Services, Microsoft, and Google, headquartered in this region, offer scalable AutoML solutions worldwide. Furthermore, leading AutoML companies such as DataRobot and H2O, based in North America, are making strides in streamlining machine learning processes, resulting in operational efficiency gains and cost reductions.

This region's dominance in the AutoML landscape is a testament to its commitment to AI innovation and its strategic role in shaping the industry's future.

 Customer Landscape of AutoML Industry

Competitive Intelligence by Technavio Analysis: Leading Players in the AutoML Market

Companies are implementing various strategies, such as strategic alliances, automl market forecast, partnerships, mergers and acquisitions, geographical expansion, and product/service launches, to enhance their presence in the industry.

Akkio Inc. - The Akkio AutoML platform empowers business users with rapid model training, deployment, and real-time predictions. Seamless integrations with tools such as Google Sheets and Salesforce streamline workflows. This platform enables users to build accurate models without requiring extensive data science expertise.

The industry research and growth report includes detailed analyses of the competitive landscape of the market and information about key companies, including:

• Akkio Inc.
• Alibaba Cloud
• Altair Engineering Inc.
• Alteryx Inc.
• Amazon Web Services Inc.
• BigML Inc.
• Databricks Inc.
• Dataiku Inc.
• DataRobot Inc.
• dotData
• Google LLC
• H2O.ai Inc.
• International Business Machines Corp.
• Microsoft Corp.
• MLJAR
• Oracle Corp.
• RapidMiner Inc.
• Salesforce Inc.
• TAZI AI
• Teradata Corp.

Qualitative and quantitative analysis of companies has been conducted to help clients understand the wider business environment as well as the strengths and weaknesses of key industry players. Data is qualitatively analyzed to categorize companies as pure play, category-focused, industry-focused, and diversified; it is quantitatively analyzed to categorize companies as dominant, leading, strong, tentative, and weak.

Recent Development and News in AutoML Market

• In August 2024, Google Cloud announced the launch of AutoML Tables, an extension to its AutoML suite that allows users to build custom machine learning models for tabular data without requiring expertise in data labeling or machine learning engineering (Google Cloud Press Release, 2024).
• In November 2024, IBM and Microsoft formed a strategic partnership to integrate IBM's Watson Studio and Microsoft's Azure Machine Learning, enabling users to access both platforms from within each other's environments, expanding their AutoML offerings and enhancing interoperability (IBM Press Release, 2024).
• In February 2025, H2O.Ai raised USD100 million in a Series E funding round, bringing their total funding to USD260 million, to accelerate the development and adoption of their AutoML platform, H2O Driverless AI (Business Wire, 2025).
• In May 2025, Amazon Web Services (AWS) introduced SageMaker AutoPilot, a new AutoML service that automates the entire machine learning workflow, including data preparation, model selection, and deployment, further expanding AWS's presence in the market (AWS Press Release, 2025).

Dive into Technavio’s robust research methodology, blending expert interviews, extensive data synthesis, and validated models for unparalleled AutoML Market insights. See full methodology.

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Why Choose Technavio for AutoML Market Insights?

"Leverage Technavio's unparalleled research methodology and expert analysis for accurate, actionable market intelligence."

The Automated Machine Learning (AutoML) market is experiencing significant growth as businesses seek to streamline their machine learning (ML) pipelines and improve model performance. AutoML solutions automate various aspects of ML workflows, including hyperparameter tuning algorithms, model selection using cross-validation, and evaluating model performance metrics. These tools help reduce model training time, improve model accuracy and precision, and build robust ML models that can be deployed at scale. AutoML platforms also ensure model fairness and equity by mitigating model bias and variance. They implement model monitoring and maintenance, automate feature engineering processes, and handle missing data imputation techniques. Furthermore, these solutions optimize model architecture for performance, manage model versions and updates, utilize transfer learning, apply ensemble methods, and combine supervised and unsupervised learning. Compared to traditional ML methods, AutoML offers substantial benefits. For instance, in a supply chain context, AutoML can help optimize inventory levels by predicting demand more accurately and efficiently. This results in a 10% reduction in stockouts and a 5% increase in sales. In the realm of compliance, AutoML can automate the process of identifying fraudulent transactions, improving detection rates by up to 25% while reducing false positives by 15%. Improving model explainability techniques and measuring model reliability and stability are essential aspects of AutoML, ensuring that businesses can trust and effectively use ML models in their operations. With these advancements, the market is poised to continue its growth, offering significant value to businesses across various industries.

What are the Key Data Covered in this AutoML Market Research and Growth Report?

• What is the expected growth of the AutoML Market between 2025 and 2029?

  • USD 13.53 billion, at a CAGR of 44.8%

• What segmentation does the market report cover?

  • The report is segmented by Type (Services, Platforms, and Software tools), Deployment (Cloud and On-premises), Application (Data processing, Model selection, and Hyperparameter tuning), Sector (BFSI, Retail and e-commerce, Manufacturing, and Healthcare), End-user (Large enterprises and SMEs), and Geography (North America, APAC, Europe, South America, and Middle East and Africa)

• Which regions are analyzed in the report?

  • North America, APAC, Europe, South America, and Middle East and Africa

• What are the key growth drivers and market challenges?

  • Increasing democratization of AI amid persistent data science talent shortage, lack of trust and inherent black box nature of complex models

• Who are the major players in the AutoML Market?

  • Akkio Inc., Alibaba Cloud, Altair Engineering Inc., Alteryx Inc., Amazon Web Services Inc., BigML Inc., Databricks Inc., Dataiku Inc., DataRobot Inc., dotData, Google LLC, H2O.ai Inc., International Business Machines Corp., Microsoft Corp., MLJAR, Oracle Corp., RapidMiner Inc., Salesforce Inc., TAZI AI, and Teradata Corp.

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