What is the current market size and future outlook for the no-code machine learning market?
The no-code machine learning market size has grown exponentially in recent years. It will grow from $1.1 $ billion in 2024 to $1.45 $ billion in 2025 at a compound annual growth rate (CAGR) of 31.0%. The growth in the historic period can be attributed to increasing demand for user-friendly tools, rise in need for cost-effective machine learning solutions, increasing use of cloud-based no-code platforms, increasing awareness of machine learning benefits among non-technical users, and rise in popularity of low-code and no-code platforms.
The no-code machine learning market size is expected to see exponential growth in the next few years. It will grow to $4.21 $ billion in 2029 at a compound annual growth rate (CAGR) of 30.6%. The growth in the forecast period can be attributed to rising demand for accessible AI tools, rising adoption of AI across various sectors, growing adoption of cloud computing, increasing availability of pre-built machine learning templates, and growing focus on reducing the technical skills barrier. Major trends in the forecast period include technological advancements, AI-driven personalization, IoT applications, predictive analytics, and self-service analytics.
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How has the no-code machine learning market evolved, and what factors have shaped its growth?
The rising adoption of the internet of things (IoT) is expected to propel the growth of the no-code machine learning market going forward. The Internet of Things (IoT) refers to the network of interconnected devices and systems that communicate and exchange data with each other over the Internet to automate processes and enhance operational efficiency. The adoption of IoT is due to its ability to improve operational efficiency, provide real-time data insights, enable automation and remote monitoring, reduce costs, improve decision-making, and drive innovation across various industries by connecting and optimizing a wide range of devices and systems. No-code machine learning is increasingly being used in the Internet of Things (IoT) to facilitate creating, deploying, and managing machine learning models without deep technical expertise. For instance, in November 2022, according to Ericsson, a Sweden-based network and telecommunications company, the number of global IoT-connected devices is expected to increase from 13.2 billion in 2022 to 34.7 billion by 2028. Therefore, the rise in adoption of the internet of things (IoT) is driving the growth of the no-code machine learning market.
What are the major segments of the no-code machine learning market?
The no-code machine learningmarket covered in this report is segmented –
1) By Offering: Platform, Services
2) By Deployment Mode: Cloud-Based, On-Premise
3) By Industry Vertical: Banking, Business & Finance And Insurance (BFSI), Healthcare, Retail, IT & Communication 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
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Which companies dominate the no-code machine learning market?
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
How will evolving trends contribute to the growth of the no-code machine learning market?
Major companies operating in the no-code machine learning market are focused on developing advanced technology to improve workflow automation, such as no-code machine learning tools. No-code machine learning tools allow users to create and deploy machine learning models without writing code, making the technology more accessible to non-technical users. For instance, in December 2023, Amazon, a US-based technology company, launched SageMaker Canvas, a no-code machine learning tool designed to enable users without coding experience to build machine learning models. Targeted at business analysts and non-technical users, this tool features a user-friendly interface for easy model creation, data preparation, and training. Critical applications include customer churn prediction, fraud detection, and inventory optimization.
What are the key regional dynamics of the no-code machine learning market, and which region leads in market share?
North America was the largest region in the no-code machine learning market in 2024. 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, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
What Does The No-Code Machine Learning Market Report 2025 Offer?
The no-code machine learning market research report from The Business Research Company offers global market size, growth rate, regional shares, competitor analysis, detailed segments, trends, and opportunities.
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.
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