A Deep Dive into No-Code Machine Learning Market 2025: Key Drivers, Growth Factors, and Projections 2025-2034

What industry-specific factors are fueling the growth of the no-code machine learning market?
Expected growth in the no-code machine learning market is being driven by the increasing uptake of the Internet of Things (IoT) technology. IoT is a term given to the extensive network of systems and devices that can interact, sharing data across the internet in order to automate tasks and boost operational performance. IoT's application is widely favored due to its ability to enhance work efficieny, provide instant data insights, support automation and off-site monitoring, and also to cut costs. It assists with decision-making and triggers innovation in different sectors by linking and fine-tuning a wide array of devices and systems. No-code machine learning is progressively being integrated into IoT technology as it aids in developing, deploying and managing machine learning models, without the need for an extensive technical background. An example of this is a prediction by Ericsson, a telecommunications company from Sweden, that IoT-connected devices worldwide will increase from 13.2 billion in 2022 to around 34.7 billion by 2028. As a result, the increasing adoption of IoT is fuelling the expansion of the no-code machine learning market.

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What Is the projected market size and growth rate for the no-code machine learning market?
There has been a dramatic increase in the size of the no-code machine learning market in recent years. It is projected to expand from $1.1 billion in 2024 to $1.45 billion in 2025, maintaining a compound annual growth rate (CAGR) of 31.0%. The historical growth can be linked to the escalating demand for user-friendly apparatus, the surging requirement for cost-efficient machine learning solutions, the growing use of cloud-based no-code platforms, enhanced understanding of the benefits of machine learning among non-technical users, and the increasing popularity of low-code and no-code platforms.

The market for no-code machine learning is predicted to witness an explosive increase in the upcoming years. It is set to surge to a whopping $4.21 billion in 2029, with a compound annual growth rate (CAGR) of 30.6%. This growth in the predicted period is due to factors such as the escalating demand for easy-to-use AI tools, amplified acceptance of AI across diverse industries, widespread use of cloud computing, increasing access to ready-made machine learning templates, and a heightened focus on bridging the gap in technical skills. Unsurprisingly, the upcoming trends for this forecast period involve technological progress, customization influenced by AI, IoT applications, prognostic analytics, and self-help analytics.

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What new trends are reshaping the no-code machine learning market and its opportunities?
Major firms in the no-code machine learning sector are paying special attention to technology advancements that enhance workflow automation. An example of such advancements is the no-code machine learning tools that enable people to design and launch machine learning models without programming knowledge. This makes the technology much more user-friendly for individuals without technical expertise. In December 2023, Amazon, an American technology firm, introduced SageMaker Canvas, a no-code machine learning tool. This tool is specifically meant for business analysts and non-technical users and comes with an easy-to-use interface for simple model creation, data management, and scrubbing. Its crucial applications cover areas such as customer attrition forecasting, fraud spotting, and inventory regulation.

What major market segments define the scope and growth 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, 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

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Which region dominates the no-code machine learning market?
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.

Which key market leaders are driving the no-code machine learning industry growth?
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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What Is Covered In The No-Code Machine Learning Global Market Report?

•Market Size Forecast: Examine the no-code machine learning market size across key regions, countries, product categories, and applications.
•Segmentation Insights: Identify and classify subsegments within the no-code machine learning market for a structured understanding.
•Key Players Overview: Analyze major players in the no-code machine learning market, including their market value, share, and competitive positioning.
•Growth Trends Exploration: Assess individual growth patterns and future opportunities in the no-code machine learning market.
•Segment Contributions: Evaluate how different segments drive overall growth in the no-code machine learning market.
•Growth Factors: Highlight key drivers and opportunities influencing the expansion of the no-code machine learning market.
•Industry Challenges: Identify potential risks and obstacles affecting the no-code machine learning market.
•Competitive Landscape: Review strategic developments in the no-code machine learning market, including expansions, agreements, and new product launches.

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