Automated Machine Learning (AUTOML) Market Size, Share, Opportunities And Trends By Provider (Open Source, Startups, Tech Giants), By Application (Fraud Detection, AML Detection, Pricing, Marketing And Sales Management, Others), And By Geography - Forecasts From 2023 To 2028

The automated machine learning market size was valued at US$653.805 million in 2021 and is expected to grow at a CAGR of 44.14% to reach US$8,450.981 million by 2028.

Automated Machine Learning (AutoML) is a process of using Artificial Intelligence (AI) algorithms to automate the process of building, optimizing, and deploying machine learning models. It is a technology that enables businesses to build predictive models with minimal human intervention automatically. The rising demand for autoML products can be attributed to the resourcefulness and usefulness of autoML in creating accurate models to make better predictions about customers, products, or other important business metrics quickly and easily for businesses that do not have proper access to data scientists or have limited expertise in machine learning to. AutoML works by automating the selection of the best ML algorithms for a given task by simultaneously designing the feature engineering and hyperparameter tuning required to optimize model performance. In addition, it can automate the deployment and scaling of models to support production use cases. The growth of the AutoML market is expected to be driven by the need for machine learning solutions with enhanced speed, efficiency, and accuracy, combined with the existing shortage of data science experts and the increasing adoption of AI and cloud services across industries.

Increasing need for data analysis and prediction by companies.

The massive increase in the amount of data generated and collected by companies is growing the demand for data analysis and prediction models, which is creating an opportunity for the expansion of the autoML market as AutoML solutions help companies to process this data quickly, efficiently and accurately, enabling them to extract valuable insights from their data. For instance, PayPal company reported that the efficiency of its fraud detection model increased from 89% to 94.7% through the adoption of H2O.ai's AutoML tool. In addition, the sales prediction model of Lenovo company witnessed an increase in accuracy by 7.5% after the adoption of autoML software by DataRobot Company. Further, California Design Den, a company providing bedding solutions, lowered its inventory carryover by approximately 50% by using the autoML tool offered by Google. 

The high cost and limited customization of autoML solutions remain a significant challenge.

AutoML solutions are expensive, which could restrain their adoption by various small and medium-sized firms and businesses as they need to weigh the benefits of using AutoML solutions against the cost to ensure that the return on investment is sufficient. Further, AutoML solutions are highly limited in customization in automating involved in building machine learning models, which limits their adoption by businesses that require effectively customized AutoML solutions since integrating non-customized AutoML solutions with existing business applications and workflows can be challenging.

By provider, the tech-giants sector holds the most significant portion of the autoML market.

Tech giants like Google, Amazon, and Microsoft have invested heavily in AutoML solutions by recognizing the growing demand for automated ML solutions at the initial stage. For instance, Microsoft Azure offers a cloud-based AutoML solution that enables businesses to build custom machine learning models without requiring extensive technical expertise to automate several tasks involved in building ML models, including feature engineering, algorithm selection, and hyperparameter tuning. Further, the increase in the efficiency of the software platforms offered by these companies is increasing the consumption of their autoML platforms.

The fraud detection segment is expected to have a major share of the automated machine learning market by application.

AutoML is extensively adopted to build predictive models for fraud detection in different industries, such as the BFSI and e-commerce, through data preparation, model selection, hyperparameter, and ensemble methods. AutoML performs data cleaning and preprocessing tasks such as data imputation, scaling, and feature engineering, ensuring that the data used for fraud detection is accurate and consistent. In addition, it can tune the hyperparameters of ML models to optimize their performance on a given dataset to ensure that the fraud detection models are robust and can generalize appropriately to new data. The rising incidents of online fraud in various e-commerce sites and companies operating in the BFSI are expected to increase the market share of this sector over the forecast period as it is generating a high demand for fraud detection solutions and models. For instance, fraud incidents among major banking companies in India in March 2021 amounted to Rs.4.92 trillion. It increased by Rs. 36342 crores during September 2022, as per research conducted by Indiaforensic. In addition, Worldline SA, a leading company offering transaction and payment services, estimated that payment fraud created a loss of 3.6% of the total sales made by e-commerce vendors in 2022.

Asia Pacific region holds a significant portion of the auto machine learning market and is expected to grow in the forecast period.

The rapid advancement of the retail and e-commerce sector in the region is expected to promote the growth of the autoML market as AutoML solutions are being extensively adopted in the retail industry to build predictive models for demand forecasting, customer segmentation, and personalized marketing by analyzing customer data to help retailers improve customer experiences and increase sales.

Market Developments:

• In March 2023, a newly acquired company by TDK Corporation, Qeexo, released a new integrated auto ML platform for Arm Keil MDK, a programming platform for microcontrollers based on the Arm® architecture adopted with different tools required to design, construct, and test embedded applications.
• In October 2022, Qualys Inc., a company providing compliance and security services through its cloud platform, declared the acquisition of autoML and AI software of Blue Hexagon, a cloud security company, to provide data integration and data insight features to consumers using Qualys Cloud Platform. 
• In September 2021, Big Squid, a company producing automated ML technology, was acquired by Qlik Technologies. This company offers integration and data analytics services to enhance the predictive analysis solution offered by Qlik Technologies by integrating autoML and AI technology.

Automated Machine Learning (AUTOML) Market Scope:

Market Segmentation:

• By Provider
  • Open Source
  • Startups
  • Tech Giants
• By Application
  • Fraud Detection
  • AML Detection
  • Pricing
  • Marketing and Sales Management
  • Others
• By Geography
  • Americas
    • USA
    • Others
  • Europe Middle East and Africa
    • Germany
    • France
    • United Kingdom
    • Others
  • Asia Pacific
    • China
    • Japan
    • South Korea
    • Others