Automated Machine Learning Market By Type (Model Deployment, Model Development, Model Training), By Component (Solution, Services), By End-User Industry (Healthcare, IT and Telecom, BFSI, Retail, Automotive, Manufacturing, Government), By Deployment Mode (On-Premises, Cloud-Based), By Technology (Neural Networks, Decision Trees, Support Vector Machines, Ensemble Learning); Global Insights & Forecast (2024 – 2030)

As per Intent Market Research, the Automated Machine Learning Market was valued at USD 1.1 billion in 2023 and will surpass USD 12.5 billion by 2030; growing at a CAGR of 41.3% during 2024 - 2030.

The Automated Machine Learning (AutoML) market has witnessed significant growth as organizations across various industries embrace machine learning and artificial intelligence (AI) to enhance decision-making, operational efficiency, and predictive capabilities. AutoML platforms automate the end-to-end machine learning process, including data preparation, model selection, training, and deployment, enabling businesses to leverage AI without requiring deep technical expertise. This has democratized access to machine learning, leading to increased adoption, particularly among small and medium-sized enterprises (SMEs), which are now able to integrate AI into their operations without a large in-house team of data scientists.

The AutoML market is growing across various segments, driven by technological advancements, the need for business automation, and the increasing use of data-driven insights across industries. The key components of AutoML are model development, deployment, and training, with each playing a crucial role in making machine learning accessible and scalable. Below, we explore the fastest-growing and largest subsegments within each segment of the market.

Model Deployment Segment is Largest Owing to Increased Adoption of AI in Production Environments

Model deployment is the largest subsegment in the AutoML market as businesses increasingly focus on moving machine learning models from development environments to production. Model deployment is critical because it allows organizations to implement machine learning models at scale, ensuring they provide continuous value by making real-time predictions. Businesses in industries such as retail, healthcare, and finance are deploying these models to enhance their customer experience, optimize operations, and improve decision-making.

The growing need for organizations to operationalize their AI models is a major driver for this subsegment's dominance. Enterprises are seeking solutions that can automate the deployment of machine learning models seamlessly, without requiring manual intervention, allowing for faster time to value. This trend is supported by the rapid development of deployment frameworks and tools that simplify the transition from model development to real-world application.

Solution Component is Largest as Organizations Seek Comprehensive AI Solutions

The solution component in the AutoML market has emerged as the largest due to its ability to offer organizations end-to-end AI capabilities. AutoML solutions generally include tools and platforms that automate the machine learning process, from data preprocessing to model selection, training, and deployment. These comprehensive solutions are highly valued by organizations looking to implement machine learning at scale, as they reduce complexity and time-to-market.

As the demand for end-to-end machine learning solutions continues to rise, more vendors are focusing on developing robust platforms that cater to various industries. This includes cloud-based solutions that enable businesses to scale machine learning models without the need for extensive infrastructure investments. Solutions from companies like DataRobot, Google, and Microsoft are driving the growth of this subsegment by making AI more accessible to organizations of all sizes.

BFSI Industry is Largest Due to Rising Demand for Predictive Analytics and Risk Management

The BFSI (Banking, Financial Services, and Insurance) industry is the largest end-user industry in the AutoML market, driven by the increasing need for predictive analytics and enhanced risk management. Financial institutions are using machine learning to predict market trends, assess credit risk, detect fraud, and personalize customer experiences. The need for real-time insights and decision-making in the BFSI sector has made machine learning and AutoML a key enabler of digital transformation.

AutoML platforms provide financial institutions with advanced analytics capabilities, automating complex processes such as data analysis and model creation, which would otherwise require significant resources. With the increased adoption of digital banking and financial services, the BFSI sector’s demand for AI and machine learning technologies is expected to continue growing, further expanding the market for AutoML solutions.

Neural Networks Technology is Largest Due to Versatility and Accuracy

Neural networks are the largest technology segment in the AutoML market due to their versatility and accuracy in solving complex tasks such as image recognition, natural language processing (NLP), and predictive analytics. This type of machine learning algorithm mimics the way the human brain processes information, making it highly effective in recognizing patterns and making predictions.

Neural networks are widely used in industries such as healthcare, retail, and automotive, where large volumes of unstructured data, like images and text, need to be processed. With the rise of deep learning and advancements in neural network architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the demand for neural network-based solutions in AutoML is expected to continue driving growth in the market.

Cloud-Based Deployment Mode is Fastest Growing as Organizations Embrace Scalable Solutions

Cloud-based deployment is the fastest-growing subsegment in the AutoML market as businesses increasingly turn to the cloud for scalable and cost-effective solutions. Cloud-based platforms allow organizations to access powerful machine learning tools without investing in on-premises infrastructure, which can be costly and complex to maintain. Additionally, cloud providers offer flexible pricing models, enabling businesses to scale their machine learning models as needed.

The ability to deploy machine learning models on the cloud also offers advantages such as enhanced collaboration, ease of integration with other cloud services, and faster model updates. As cloud adoption accelerates across industries like healthcare, manufacturing, and retail, the demand for cloud-based AutoML platforms is expected to grow rapidly, positioning this deployment mode as the market’s fastest-growing segment.

North America is Largest Region Due to Strong AI Adoption and Technological Advancements

North America remains the largest region in the AutoML market, owing to its strong adoption of artificial intelligence technologies, a highly developed IT infrastructure, and a large presence of key players such as Google, Microsoft, and Amazon Web Services. The region’s advanced technology ecosystem, coupled with significant investments in AI research and development, has created an ideal environment for the widespread deployment of AutoML solutions.

The BFSI and IT and Telecom sectors in North America are major contributors to the growth of AutoML, as these industries are heavily focused on leveraging AI for predictive analytics, automation, and operational efficiency. The growing number of startups and innovation hubs further strengthens the region's dominance in the AutoML market.

Competitive Landscape and Leading Companies

The competitive landscape of the Automated Machine Learning market is characterized by the presence of several large players offering robust solutions. Companies such as DataRobot, Google LLC, Amazon Web Services (AWS), Microsoft Corporation, and IBM Corporation dominate the market with their advanced AutoML platforms that cater to diverse industries. These companies continue to innovate and expand their offerings through acquisitions, partnerships, and product developments.

In addition to these major players, several startups are entering the market, bringing innovative solutions that target specific use cases, such as fraud detection in banking or demand forecasting in retail. As the market continues to evolve, the key players are focusing on enhancing the scalability, usability, and affordability of their AutoML solutions to remain competitive. Additionally, the adoption of cloud-based platforms and integration with other AI technologies is likely to intensify the competition in the coming years.

Recent Developments:

• DataRobot has recently expanded its offerings by acquiring a company specializing in optimization algorithms for automated machine learning, enhancing its predictive capabilities.
• Microsoft has introduced new automated machine learning services for its Azure platform, making it easier for businesses to integrate AI into their workflows.
• Google Cloud launched improvements to its AutoML tools, particularly focused on image and text data processing, to help companies accelerate their AI adoption.
• H2O.ai has partnered with SAP to offer more advanced AutoML solutions for enterprises, helping businesses deploy machine learning models faster.
• AWS unveiled a new automated machine learning tool designed for non-experts to build and deploy machine learning models with ease, further democratizing AI technology.

List of Leading Companies:

• Alteryx, Inc.
• Amazon Web Services (AWS)
• Anaconda
• Artelnics
• BigML
• DataRobot
• Google LLC
• H2O.ai
• IBM Corporation
• Microsoft Azure
• Microsoft Corporation
• RapidMiner
• SAS Institute Inc.
• TPOT (Python-based Library)
• Zebra Medical Vision

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