Automated Machine Learning Market Size, Share, and Industry Analysis, By Deployment Model (On-premises and Cloud-based), By Enterprise Type (Small and Medium-sized Enterprises and Large Enterprises); By Industry Vertical (BFSI, Healthcare & Life Sciences, Retail & E-commerce, IT & Telecom, Government & Defense, and Others), and Regional Forecast, 2024-2032

Automated machine (AutoML) learning offers methods and techniques accessible to non-machine learning specialists and speeds up machine learning research. In recent times, machine learning (ML) has been incredibly successful and more disciplines are depending on it. Developers with less experience with ML can train complex models tailored to their business requirements due to AutoML. The rapid expansion of ML applications has developed a need for readily available ML techniques that do not require specialized understanding.

The AutoML market is experiencing significant growth owing to the data science talent gap, rapid technological advancements, and growing adoption of AI in various industries. As per the Adastra Corporation's "Data Professionals Market Survey Forecast 2024" report, around 76% of data professionals in the U.S. noted that the data science talent shortage to continue throughout 2024. According to Anaconda Inc.'s "2022 State of Data Science" report, 63% of respondents stated that their organization is at least moderately worried about the field's talent shortage. As businesses generate more data, there is an increasing demand for data scientists. AutoML tools can help bridge this gap by enabling non-machine learning experts to build and deploy ML models.   

The COVID-19 pandemic had a mixed impact on market. The outbreak highlighted the significance of data-driven decision-making, resulting in significant interest in AI and ML, including AutoML. However, many organizations face budget constraints on account of the economic impact of the pandemic, leading to some delays in AI and AutoML investments.

Impact of Generative AI on the Automated Machine Learning Market

Machine Learning (ML) is a revolutionary technology that presents significant challenges, such as model performance, data imbalance, and complexity. According to the Ecosystm-Kyndryl Digital Transformation Study 2022, around 46% of survey respondents showed their interest in AI/ML technologies. The generative AI creates more complex models, including deep neural networks, which are difficult for humans to design manually. This augments the capabilities of AutoML to tackle more sophisticated problems. 

Key Insights

The report covers the following key insights:

• Micro Macro Economic Indicators


• Drivers, Restraints, Trends, and Opportunities


• Business Strategies Adopted by the Key Players


• Consolidated SWOT Analysis of Key Players

Segmentation

Analysis By Enterprise Type

The automated machine learning market is segmented into small and medium-sized enterprises and large enterprises. The large enterprises segment dominates the market as these enterprises typically have more financial resources to invest in cutting-edge technologies. Large organizations can afford to allocate more funds toward purchasing AutoML platforms, hiring data scientists, and investing in required infrastructure. Large enterprises often generate huge amounts of data, which is important for training accurate ML models. The access to extensive and diverse datasets enables them to develop more robust models using AutoML.

Regional Analysis

The global automated machine learning market is segmented into five regions: North America, South America, Europe, the Middle East & Africa, and Asia Pacific.

North America commands the highest market share owing to the rapid technological advancements, presence of key players, and robust research and development activities in this region. The businesses in the region are early adopters of ML technology, resulting in a high demand for AutoML systems.

The Asia Pacific region demonstrates the highest CAGR in the automated machine learning market on account of its rapidly expanding economies and robust digitalization. Businesses in this region are undergoing remarkable digital transformation to address the rapidly evolving business dynamics driving investment in advanced technologies.

Global Distribution of Automated Machine Learning Market, By Region

• North America – 32%


• South America – 7%


• Europe – 24%


• Middle East and Africa – 12%


• Asia Pacific – 25%

Key Players Covered

The report provides the profiles of key players such as Google LLC, Run.ai, Amazon Web Services, Inc., Binary Global, Microsoft Corporation, IBM Corporation, and DataBricks.

Key Industry Developments

March 2023: TDK Corporation’s company Qeexo announced the launch of Automated ML for Arm Keil MDK. This solution would allow end-to-end embedded ML and development workflows with AutoML and Arm Keil MDK.

May 2021: DataBricks announced the launch of “DataBricks AutoML” to empower businesses to easily build and deploy ML models by automating pre-processing and model training/tuning.