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No-Code Machine Learning Market Poised To Hit $5.49 Billion By 2030 With A 30.5% CAGR
The no-code machine learning market has experienced significant expansion in its size over recent years. It is projected to increase from $1.45 billion in 2025 to $1.89 billion in 2026, demonstrating a compound annual growth rate (CAGR) of 30.8%. The growth observed in the historic period can be attributed to increasing demand for AI and ml solutions, shortage of skilled data scientists, rise of cloud computing adoption, growth of enterprise automation, and expansion of analytics in business operations.
The no-code machine learning market is forecast to undergo substantial expansion over the upcoming years. It will grow to $5.49 billion in 2030 at a compound annual growth rate (CAGR) of 30.5%. This growth during the forecast period is propelled by factors such as the integration with predictive analytics tools, an increase in business intelligence platforms, the escalating demand for rapid model deployment, its widespread adoption across the healthcare and BFSI sectors, and the rise of self-service ML platforms. Significant trends expected in this period involve the wider embrace of low-code/no-code solutions, advancements in automated model tuning, the empowerment of citizen data scientists, the implementation of drag-and-drop AI workflows, and the provision of pre-built ML templates.
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No-Code Machine Learning Market Industry Drivers: What’s Behind The Revenue Growth?
The increasing uptake of the Internet of Things (IoT) is anticipated to drive the expansion of the no-code machine learning market going forward. The Internet of Things (IoT) describes a system of interconnected devices and systems that communicate and exchange data over the internet to automate functions and boost operational efficiency. The widespread adoption of IoT stems from its capacity to enhance operational efficiency, offer real-time data insights, enable automation and remote monitoring, lower expenses, improve decision-making, and foster innovation by linking and optimizing diverse devices and systems across sectors. No-code machine learning finds growing application within the Internet of Things (IoT) by simplifying the creation, deployment, and management of machine learning models for users without extensive technical knowledge. For example, the Organisation for Economic Co operation and Development (OECD), a France-based intergovernmental organization, reported in its December 2023 Measuring the Internet of Things report that 33 % of businesses in OECD countries had adopted IoT technologies, an increase from 28 % in 2022, marking a year on year rise of 5 percentage points. Consequently, the growing adoption of the Internet of Things (IoT) is fueling the expansion of the no-code machine learning market.
No-Code Machine Learning Market Segment Analysis Spotlighting Growth Areas
The no-code machine learning market covered in this report is segmented –
1) By Offering: Platform, Services
2) By Deployment Mode: Cloud-Based, On-Premise
3) By Industry Vertical: Banking, Financial Services And Insurance (BFSI), Healthcare, Retail, Information Technology(IT) And Telecom, Manufacturing, Government
4) By Application: Predictive Analytics, Process Automation, Data Visualization, Business Intelligence, Customer Relationship Management, Supply Chain Optimization
Subsegments:
1) By Platform: Automated Machine Learning Platforms (AutoML), Drag-and-Drop Machine Learning Platforms, Model Deployment Platforms, Data Preparation Platforms, Visualization Aand Reporting Platforms, Integration Platforms for APIs And Data Sources
2) By Services: Consulting Services, Implementation Services, Training and Education Services, Support And Maintenance Services, Custom Solution Development Services
#No-Code Machine Learning Market Growth Trends: What’s Shaping The Future Outlook?
Key entities operating in the no-code machine learning market are prioritizing the creation of sophisticated technology to refine workflow automation, exemplified by no-code machine learning tools. These tools enable users to formulate and deploy machine learning models without needing to write code, thus broadening the technology’s reach to non-technical individuals. As an illustration, in December 2023, Amazon, a US-based technology company, introduced SageMaker Canvas, a no-code machine learning tool engineered to permit users without coding experience to construct machine learning models. Designed for business analysts and other non-technical users, this offering provides a user-friendly interface for straightforward model creation, data preparation, and training. Its critical applications involve customer churn prediction, fraud detection, and inventory optimization.
No-Code Machine Learning Market Leading Companies: Who Holds The Strongest Market Presence?
Major companies operating in the no-code machine learning market are Apple Create ML, Microsoft Azure Machine Learning Studio, Amazon Web Services, SAS Viya, DataRobot Inc, LityxIQ, H2O.ai, Dataiku DSS, C3 AI Suite, RapidMiner Studio, BigML Inc., Google Teachable Machine, Edge Impulse, Microsoft Lobe, KNIME Analytics Platform, MonkeyLearn, Akkio AI, Obviously AI, Runway ML, Fritz AI, Sway AI, PyCaret, Ever AI, Neural Designer
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No-Code Machine Learning Market Geographic Analysis: Where Is Demand Rising Fastest?
North America was the largest region in the no-code machine learning market in 2025. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the no-code machine learning market report are Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
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Wasay has over a decade of experience in market research, data modelling, and analytics, with prior experience at GlobalData and Decision Tree Consulting Services. At The Business Research Company , he leads research operations across syndicated studies, customized consulting engagements, and the Global Market Model platform. His professional experience includes supporting organizations such as Boston Consulting Group, KPMG, and Ernst & Young. Wasay holds a degree in Electronics and Communications Engineering, postgraduate management qualifications from International Management Institute Belgium and Indian School of Business and Entrepreneurship, and completed the Integrated Program in Business Analytics from Indian Institute of Management Indore.
