The no-code machine learning market size has grown exponentially in recent years. It will grow from $1.45 billion in 2025 to $1.89 billion in 2026 at a compound annual growth rate (CAGR) of 30.8%. The growth 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, expansion of analytics in business operations.
The no-code machine learning market size is expected to see exponential growth in the next few years. It will grow to $5.49 billion in 2030 at a compound annual growth rate (CAGR) of 30.5%. The growth in the forecast period can be attributed to integration with predictive analytics tools, growth in business intelligence platforms, demand for rapid model deployment, adoption across healthcare and bfsI sectors, emergence of self-service ml platforms. Major trends in the forecast period include low-code/no-code adoption, automated model tuning, citizen data scientist enablement, drag-and-drop AI workflows, pre-built ml templates.
The expanding use of the Internet of Things (IoT) is expected to contribute to the growth of the no-code machine learning market over the forecast period. The Internet of Things refers to interconnected devices and systems that exchange data over the internet to automate processes and improve operational efficiency. IoT adoption is driven by benefits such as real-time data insights, automation, remote monitoring, cost reduction, and improved decision-making across industries. No-code machine learning tools are increasingly used within IoT environments to enable the creation, deployment, and management of machine learning models without requiring advanced technical expertise. For example, in December 2023, according to the Organisation for Economic Co-operation and Development (OECD), a France-based intergovernmental organization, 33% of businesses across OECD countries had adopted IoT technologies, up from 28% in 2022, reflecting a year-on-year increase of 5 percentage points. Accordingly, rising IoT adoption is supporting the expansion of the no-code machine learning market.
Leading companies operating in the no-code machine learning market are focusing on developing advanced technologies to improve workflow automation, such as no-code machine learning tools. No-code machine learning tools allow users to build and deploy machine learning models without writing code. For example, in December 2023, Amazon launched SageMaker Canvas, a no-code machine learning tool that enables business analysts and non-technical users to create models for applications such as customer churn prediction, fraud detection, and inventory optimization through an intuitive interface.
In July 2024, Forwrd.ai, a US-based data science automation platform, acquired LoudnClear.AI for an undisclosed amount. This acquisition allows LoudnClear.AI to continue advancing its mission of enabling revenue operations and business teams to analyze unstructured data more efficiently and gain deeper insights into customer sentiment using natural language processing, machine learning, and artificial intelligence. LoudnClear.AI is an Israel-based provider of no-code machine learning solutions.
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.
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. The countries covered in the no-code machine learning market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, UK, USA, Canada, Italy, Spain.
Tariffs have impacted the no-code machine learning market by increasing the cost of importing cloud infrastructure, AI hardware, and pre-built software solutions. Regions like Asia-Pacific and Europe, which rely heavily on imported AI components and platforms, are most affected, slowing deployment and adoption of no-code ML tools. The platform and services segments face higher operational costs due to these tariffs. On the positive side, tariffs are encouraging local development of no-code ML platforms and investments in domestic AI infrastructure, which can enhance regional capabilities and reduce dependency on imports over time.
The no-code machine learning market research report is one of a series of new reports that provides no-code machine learning market statistics, including no-code machine learning industry global market size, regional shares, competitors with a no-code machine learning market share, detailed no-code machine learning market segments, market trends and opportunities, and any further data you may need to thrive in the no-code machine learning industry. This no-code machine learning market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.
No-code machine learning refers to the practice of developing, deploying, and managing machine learning models without writing any code. This approach typically involves using graphical interfaces, drag-and-drop tools, and pre-built templates provided by no-code platforms. These platforms abstract the complexities of programming and data science, enabling users, often non-technical professionals, to build and use machine learning models by following intuitive steps.
The main offering of no-code machine learning offerings include platforms and services. A no-code machine learning platform is a software tool that enables users to create, train, and deploy machine learning models without writing any code, using a visual interface to simplify the process for non-technical users. It can be deployed both on the cloud and on-premise and is used by various industries such as banking, financial services and insurance (BFSI), healthcare, retail, information technology (IT), telecom, manufacturing, and government. It is used for various applications, including predictive analytics, process automation, data visualization, business intelligence, customer relationship management, and supply chain optimization.
The no-code machine learning market consists of revenues earned by entities by providing services such as model building, data preparation, data visualization, model training and evaluation. The market value includes the value of related goods sold by the service provider or included within the service offering. The no-code machine learning market also includes sales of data preparation tools, automated machine learning solutions, drag-and-drop workflow builders and predictive analytics tools. Values in this market are ‘factory gate’ values, that is the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.
The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).
The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.
This product will be delivered within 1-3 business days.
The no-code machine learning market size is expected to see exponential growth in the next few years. It will grow to $5.49 billion in 2030 at a compound annual growth rate (CAGR) of 30.5%. The growth in the forecast period can be attributed to integration with predictive analytics tools, growth in business intelligence platforms, demand for rapid model deployment, adoption across healthcare and bfsI sectors, emergence of self-service ml platforms. Major trends in the forecast period include low-code/no-code adoption, automated model tuning, citizen data scientist enablement, drag-and-drop AI workflows, pre-built ml templates.
The expanding use of the Internet of Things (IoT) is expected to contribute to the growth of the no-code machine learning market over the forecast period. The Internet of Things refers to interconnected devices and systems that exchange data over the internet to automate processes and improve operational efficiency. IoT adoption is driven by benefits such as real-time data insights, automation, remote monitoring, cost reduction, and improved decision-making across industries. No-code machine learning tools are increasingly used within IoT environments to enable the creation, deployment, and management of machine learning models without requiring advanced technical expertise. For example, in December 2023, according to the Organisation for Economic Co-operation and Development (OECD), a France-based intergovernmental organization, 33% of businesses across OECD countries had adopted IoT technologies, up from 28% in 2022, reflecting a year-on-year increase of 5 percentage points. Accordingly, rising IoT adoption is supporting the expansion of the no-code machine learning market.
Leading companies operating in the no-code machine learning market are focusing on developing advanced technologies to improve workflow automation, such as no-code machine learning tools. No-code machine learning tools allow users to build and deploy machine learning models without writing code. For example, in December 2023, Amazon launched SageMaker Canvas, a no-code machine learning tool that enables business analysts and non-technical users to create models for applications such as customer churn prediction, fraud detection, and inventory optimization through an intuitive interface.
In July 2024, Forwrd.ai, a US-based data science automation platform, acquired LoudnClear.AI for an undisclosed amount. This acquisition allows LoudnClear.AI to continue advancing its mission of enabling revenue operations and business teams to analyze unstructured data more efficiently and gain deeper insights into customer sentiment using natural language processing, machine learning, and artificial intelligence. LoudnClear.AI is an Israel-based provider of no-code machine learning solutions.
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.
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. The countries covered in the no-code machine learning market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, UK, USA, Canada, Italy, Spain.
Tariffs have impacted the no-code machine learning market by increasing the cost of importing cloud infrastructure, AI hardware, and pre-built software solutions. Regions like Asia-Pacific and Europe, which rely heavily on imported AI components and platforms, are most affected, slowing deployment and adoption of no-code ML tools. The platform and services segments face higher operational costs due to these tariffs. On the positive side, tariffs are encouraging local development of no-code ML platforms and investments in domestic AI infrastructure, which can enhance regional capabilities and reduce dependency on imports over time.
The no-code machine learning market research report is one of a series of new reports that provides no-code machine learning market statistics, including no-code machine learning industry global market size, regional shares, competitors with a no-code machine learning market share, detailed no-code machine learning market segments, market trends and opportunities, and any further data you may need to thrive in the no-code machine learning industry. This no-code machine learning market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.
No-code machine learning refers to the practice of developing, deploying, and managing machine learning models without writing any code. This approach typically involves using graphical interfaces, drag-and-drop tools, and pre-built templates provided by no-code platforms. These platforms abstract the complexities of programming and data science, enabling users, often non-technical professionals, to build and use machine learning models by following intuitive steps.
The main offering of no-code machine learning offerings include platforms and services. A no-code machine learning platform is a software tool that enables users to create, train, and deploy machine learning models without writing any code, using a visual interface to simplify the process for non-technical users. It can be deployed both on the cloud and on-premise and is used by various industries such as banking, financial services and insurance (BFSI), healthcare, retail, information technology (IT), telecom, manufacturing, and government. It is used for various applications, including predictive analytics, process automation, data visualization, business intelligence, customer relationship management, and supply chain optimization.
The no-code machine learning market consists of revenues earned by entities by providing services such as model building, data preparation, data visualization, model training and evaluation. The market value includes the value of related goods sold by the service provider or included within the service offering. The no-code machine learning market also includes sales of data preparation tools, automated machine learning solutions, drag-and-drop workflow builders and predictive analytics tools. Values in this market are ‘factory gate’ values, that is the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.
The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).
The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.
This product will be delivered within 1-3 business days.
Table of Contents
1. Executive Summary
2. No-Code Machine Learning Market Characteristics
3. No-Code Machine Learning Market Supply Chain Analysis
4. Global No-Code Machine Learning Market Trends and Strategies
5. No-Code Machine Learning Market Analysis of End Use Industries
7. Global No-Code Machine Learning Strategic Analysis Framework, Current Market Size, Market Comparisons and Growth Rate Analysis
8. Global No-Code Machine Learning Total Addressable Market (TAM) Analysis for the Market
9. No-Code Machine Learning Market Segmentation
10. No-Code Machine Learning Market, Industry Metrics by Country
11. No-Code Machine Learning Market Regional and Country Analysis
12. Asia-Pacific No-Code Machine Learning Market
13. China No-Code Machine Learning Market
14. India No-Code Machine Learning Market
15. Japan No-Code Machine Learning Market
16. Australia No-Code Machine Learning Market
17. Indonesia No-Code Machine Learning Market
18. South Korea No-Code Machine Learning Market
19. Taiwan No-Code Machine Learning Market
20. South East Asia No-Code Machine Learning Market
21. Western Europe No-Code Machine Learning Market
22. UK No-Code Machine Learning Market
23. Germany No-Code Machine Learning Market
24. France No-Code Machine Learning Market
25. Italy No-Code Machine Learning Market
26. Spain No-Code Machine Learning Market
27. Eastern Europe No-Code Machine Learning Market
28. Russia No-Code Machine Learning Market
29. North America No-Code Machine Learning Market
30. USA No-Code Machine Learning Market
31. Canada No-Code Machine Learning Market
32. South America No-Code Machine Learning Market
33. Brazil No-Code Machine Learning Market
34. Middle East No-Code Machine Learning Market
35. Africa No-Code Machine Learning Market
37. No-Code Machine Learning Market Competitive Landscape and Company Profiles
38. No-Code Machine Learning Market Other Major and Innovative Companies
41. No-Code Machine Learning Market High Potential Countries, Segments and Strategies
42. Appendix
Executive Summary
No-Code Machine Learning Market Global Report 2026 provides strategists, marketers and senior management with the critical information they need to assess the market.This report focuses no-code machine learning market which is experiencing strong growth. The report gives a guide to the trends which will be shaping the market over the next ten years and beyond.
Reasons to Purchase:
- Gain a truly global perspective with the most comprehensive report available on this market covering 16 geographies.
- Assess the impact of key macro factors such as geopolitical conflicts, trade policies and tariffs, inflation and interest rate fluctuations, and evolving regulatory landscapes.
- Create regional and country strategies on the basis of local data and analysis.
- Identify growth segments for investment.
- Outperform competitors using forecast data and the drivers and trends shaping the market.
- Understand customers based on end user analysis.
- Benchmark performance against key competitors based on market share, innovation, and brand strength.
- Evaluate the total addressable market (TAM) and market attractiveness scoring to measure market potential.
- Suitable for supporting your internal and external presentations with reliable high-quality data and analysis
- Report will be updated with the latest data and delivered to you along with an Excel data sheet for easy data extraction and analysis.
- All data from the report will also be delivered in an excel dashboard format.
Description
Where is the largest and fastest growing market for no-code machine learning? How does the market relate to the overall economy, demography and other similar markets? What forces will shape the market going forward, including technological disruption, regulatory shifts, and changing consumer preferences? The no-code machine learning market global report answers all these questions and many more.The report covers market characteristics, size and growth, segmentation, regional and country breakdowns, total addressable market (TAM), market attractiveness score (MAS), competitive landscape, market shares, company scoring matrix, trends and strategies for this market. It traces the market’s historic and forecast market growth by geography.
- The market characteristics section of the report defines and explains the market. This section also examines key products and services offered in the market, evaluates brand-level differentiation, compares product features, and highlights major innovation and product development trends.
- The supply chain analysis section provides an overview of the entire value chain, including key raw materials, resources, and supplier analysis. It also provides a list competitor at each level of the supply chain.
- The updated trends and strategies section analyses the shape of the market as it evolves and highlights emerging technology trends such as digital transformation, automation, sustainability initiatives, and AI-driven innovation. It suggests how companies can leverage these advancements to strengthen their market position and achieve competitive differentiation.
- The regulatory and investment landscape section provides an overview of the key regulatory frameworks, regularity bodies, associations, and government policies influencing the market. It also examines major investment flows, incentives, and funding trends shaping industry growth and innovation.
- The market size section gives the market size ($b) covering both the historic growth of the market, and forecasting its development.
- The forecasts are made after considering the major factors currently impacting the market. These include the technological advancements such as AI and automation, Russia-Ukraine war, trade tariffs (government-imposed import/export duties), elevated inflation and interest rates.
- The total addressable market (TAM) analysis section defines and estimates the market potential compares it with the current market size, and provides strategic insights and growth opportunities based on this evaluation.
- The market attractiveness scoring section evaluates the market based on a quantitative scoring framework that considers growth potential, competitive dynamics, strategic fit, and risk profile. It also provides interpretive insights and strategic implications for decision-makers.
- Market segmentations break down the market into sub markets.
- The regional and country breakdowns section gives an analysis of the market in each geography and the size of the market by geography and compares their historic and forecast growth.
- Expanded geographical coverage includes Taiwan and Southeast Asia, reflecting recent supply chain realignments and manufacturing shifts in the region. This section analyzes how these markets are becoming increasingly important hubs in the global value chain.
- The competitive landscape chapter gives a description of the competitive nature of the market, market shares, and a description of the leading companies. Key financial deals which have shaped the market in recent years are identified.
- The company scoring matrix section evaluates and ranks leading companies based on a multi-parameter framework that includes market share or revenues, product innovation, and brand recognition.
Report Scope
Markets Covered:
1) By Offering: Platform; Services2) 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 Sources2) By Services: Consulting Services; Implementation Services; Training and Education Services; Support And Maintenance Services; Custom Solution Development Services
Companies Mentioned: 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
Countries: Australia; Brazil; China; France; Germany; India; Indonesia; Japan; Taiwan; Russia; South Korea; UK; USA; Canada; Italy; Spain.
Regions: Asia-Pacific; South East Asia; Western Europe; Eastern Europe; North America; South America; Middle East; Africa
Time Series: Five years historic and ten years forecast.
Data: Ratios of market size and growth to related markets, GDP proportions, expenditure per capita.
Data Segmentation: Country and regional historic and forecast data, market share of competitors, market segments.
Sourcing and Referencing: Data and analysis throughout the report is sourced using end notes.
Delivery Format: Word, PDF or Interactive Report + Excel Dashboard
Added Benefits:
- Bi-Annual Data Update
- Customisation
- Expert Consultant Support
Companies Mentioned
The companies featured in this No-Code Machine Learning market report include:- 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
Table Information
| Report Attribute | Details |
|---|---|
| No. of Pages | 250 |
| Published | February 2026 |
| Forecast Period | 2026 - 2030 |
| Estimated Market Value ( USD | $ 1.89 Billion |
| Forecasted Market Value ( USD | $ 5.49 Billion |
| Compound Annual Growth Rate | 30.5% |
| Regions Covered | Global |
| No. of Companies Mentioned | 25 |
