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市場調查報告書
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1982643

2026年全球無程式碼機器學習市場報告

No-Code Machine Learning Global Market Report 2026
出版日期: | 出版商: The Business Research Company | 英文 250 Pages | 商品交期: 2-10個工作天內

價格
簡介目錄

近年來，無程式碼機器學習市場發展迅速。預計該市場規模將從2025年的14.5億美元成長到2026年的18.9億美元，複合年成長率（CAGR）高達30.8%。這一成長主要歸功於對人工智慧和機器學習解決方案日益成長的需求、熟練數據科學家的短缺、雲端運算的廣泛應用、企業自動化技術的進步以及分析技術在業務運營中應用的不斷普及。

預計未來幾年，無程式碼機器學習市場將迎來爆炸性成長，到2030年市場規模將達到54.9億美元，複合年成長率（CAGR）高達30.5%。預測期內的成長預計將受到以下因素的推動：與預測分析工具的整合、商業智慧平台的興起、對快速模型部署的需求、醫療保健和銀行、金融服務/保險（BFS/I）行業的應用，以及自助式機器學習平台的崛起。預測期內的關鍵趨勢包括：低程式碼/無程式碼技術的應用、自動化模型調優、公民資料科學家的發展、拖放式人工智慧工作流程以及預先建置的機器學習模板。

預計在預測期內，物聯網 (IoT) 的日益普及將推動無程式碼機器學習市場的成長。物聯網指的是互聯的設備和系統透過網際網路交換數據，從而實現流程自動化並提高營運效率。物聯網的普及得益於其許多優勢，例如即時數據洞察、自動化、遠端監控、成本降低以及跨行業決策的改進。由於無程式碼機器學習工具無需高級技術專長即可建立、部署和管理機器學習模型，因此在物聯網環境中，此類工具的使用正在增加。例如，根據總部位於法國的政府間經濟合作暨發展組織(OECD) 的數據，截至 2023 年 12 月，OECD 成員國中已有 33% 的公司採用了物聯網技術，比 2022 年的 28% 成長了 5 個百分點。因此，物聯網的日益普及正在推動無程式碼機器學習市場的擴張。

主要企業正致力於開發能夠提升工作流程自動化程度的先進技術，例如無程式碼機器學習工具。無程式碼機器學習工具允許使用者無需說明任何程式碼即可建置和部署機器學習模型。例如，亞馬遜於 2023 年 12 月發布了 SageMaker Canvas，這是一款無程式碼機器學習工具，它使不具備技術專長的業務分析師和用戶能夠透過直覺的介面創建模型，用於客戶流失預測、詐欺檢測和庫存最佳化等應用。

目錄

第1章執行摘要

第2章 市場特徵

  • 市場定義和範圍
  • 市場區隔
  • 主要產品和服務概述
  • 全球無程式碼機器學習市場：吸引力評分及分析
  • 成長潛力分析、競爭評估、策略適宜性評估、風險狀況評估

第3章 市場供應鏈分析

  • 供應鏈與生態系概述
  • 清單：主要原料、資源和供應商
  • 主要經銷商和通路合作夥伴名單
  • 主要最終用戶列表

第4章：全球市場趨勢與策略

  • 關鍵科技與未來趨勢
    • 人工智慧（AI）和自主人工智慧
    • 數位化、雲端運算、巨量資料、網路安全
    • 工業4.0和智慧製造
    • 物聯網、智慧基礎設施、互聯生態系統
    • 金融科技、區塊鏈、監管科技、數位金融
  • 主要趨勢
    • 低代碼/無代碼的實現
    • 模型自動調優
    • 允許一般使用者進行數據分析
    • 拖放式人工智慧工作流程
    • 預先存在的機器學習模板

第5章 終端用戶產業市場分析

  • 銀行、金融服務和保險（BFSI）
  • 衛生保健
  • 零售
  • 資訊科技（IT）和通訊
  • 製造業

第6章 市場：宏觀經濟情景，包括利率、通貨膨脹、地緣政治、貿易戰和關稅的影響、關稅戰和貿易保護主義對供應鏈的影響，以及 COVID-19 疫情對市場的影響。

第7章：全球策略分析架構、目前市場規模、市場對比及成長率分析

  • 全球無程式碼機器學習市場：PESTEL 分析（政治、社會、技術、環境、法律因素、促進因素和限制因素）
  • 全球無程式碼機器學習市場規模、對比及成長率分析
  • 全球無程式碼機器學習市場表現：規模與成長，2020-2025年
  • 全球無程式碼機器學習市場預測：規模與成長，2025-2030年，2035年預測

第8章：全球市場總規模（TAM）

第9章 市場細分

  • 報價
  • 平台、服務
  • 部署模式
  • 基於雲端，本地部署
  • 按行業
  • 銀行、金融和保險 (BFSI)、醫療保健、零售、資訊科技 (IT) 和通訊、製造業、政府
  • 透過使用
  • 預測分析、流程自動化、資料視覺化、商業智慧、客戶關係管理、供應鏈最佳化
  • 按類型細分：平台
  • 自動化機器學習平台（AutoML）、拖放機器學習平台、模型配置平台、資料準備平台、視覺化和報表平台、API和資料來源整合平台
  • 按類型細分：服務
  • 諮詢服務、實施服務、培訓和教育服務、支援和維護服務、客製化解決方案開發服務

第10章 市場與產業指標：依國家分類

第11章 區域與國別分析

  • 全球無程式碼機器學習市場：按地區分類，實際結果與預測，2020-2025年、2025-2030年預測、2035年預測
  • 全球無程式碼機器學習市場：按國家/地區分類，實際結果和預測，2020-2025 年、2025-2030 年預測、2035 年預測

第12章 亞太市場

第13章：中國市場

第14章：印度市場

第15章：日本市場

第16章：澳洲市場

第17章：印尼市場

第18章：韓國市場

第19章 台灣市場

第20章：東南亞市場

第21章 西歐市場

第22章英國市場

第23章：德國市場

第24章：法國市場

第25章：義大利市場

第26章：西班牙市場

第27章 東歐市場

第28章：俄羅斯市場

第29章 北美市場

第30章：美國市場

第31章：加拿大市場

第32章：南美洲市場

第33章：巴西市場

第34章 中東市場

第35章：非洲市場

第36章 市場監理與投資環境

第37章：競爭格局與公司概況

  • 無程式碼機器學習市場：競爭格局與市場佔有率（2024 年）
  • 無程式碼機器學習市場：公司估值矩陣
  • 無程式碼機器學習市場：公司概況
    • Apple Create ML
    • Microsoft Azure Machine Learning Studio
    • Amazon Web Services
    • SAS Viya
    • DataRobot Inc

第38章 其他大型企業和創新企業

  • 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

第39章 全球市場競爭基準分析與儀錶板

第40章 重大併購

第41章 具有高市場潛力的國家、細分市場與策略

  • 2030 年無程式碼機器學習市場：提供新機會的國家
  • 2030 年無程式碼機器學習市場：提供新機會的細分領域
  • 2030 年無程式碼機器學習市場：成長策略
    • 基於市場趨勢的策略
    • 競爭對手的策略

第42章附錄

簡介目錄
Product Code: IT5MNCML01_G26Q1

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.

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 from The Business Research Company 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.

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.

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.

No-Code Machine Learning Market Global Report 2026 from The Business Research Company 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 within 2-3 working days of order 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.

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 from the Business Research Company 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.

Scope

  • Markets Covered: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
  • 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 Segmentations: 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

Added Benefits available all on all list-price licence purchases, to be claimed at time of purchase. Customisations within report scope and limited to 20% of content and consultant support time limited to 8 hours.

Table of Contents

1. Executive Summary

  • 1.1. Key Market Insights (2020-2035)
  • 1.2. Visual Dashboard: Market Size, Growth Rate, Hotspots
  • 1.3. Major Factors Driving the Market
  • 1.4. Top Three Trends Shaping the Market

2. No-Code Machine Learning Market Characteristics

  • 2.1. Market Definition & Scope
  • 2.2. Market Segmentations
  • 2.3. Overview of Key Products and Services
  • 2.4. Global No-Code Machine Learning Market Attractiveness Scoring And Analysis
    • 2.4.1. Overview of Market Attractiveness Framework
    • 2.4.2. Quantitative Scoring Methodology
    • 2.4.3. Factor-Wise Evaluation
  • Growth Potential Analysis, Competitive Dynamics Assessment, Strategic Fit Assessment And Risk Profile Evaluation
    • 2.4.4. Market Attractiveness Scoring and Interpretation
    • 2.4.5. Strategic Implications and Recommendations

3. No-Code Machine Learning Market Supply Chain Analysis

  • 3.1. Overview of the Supply Chain and Ecosystem
  • 3.2. List Of Key Raw Materials, Resources & Suppliers
  • 3.3. List Of Major Distributors and Channel Partners
  • 3.4. List Of Major End Users

4. Global No-Code Machine Learning Market Trends And Strategies

  • 4.1. Key Technologies & Future Trends
    • 4.1.1 Artificial Intelligence & Autonomous Intelligence
    • 4.1.2 Digitalization, Cloud, Big Data & Cybersecurity
    • 4.1.3 Industry 4.0 & Intelligent Manufacturing
    • 4.1.4 Internet Of Things (Iot), Smart Infrastructure & Connected Ecosystems
    • 4.1.5 Fintech, Blockchain, Regtech & Digital Finance
  • 4.2. Major Trends
    • 4.2.1 Low-Code/No-Code Adoption
    • 4.2.2 Automated Model Tuning
    • 4.2.3 Citizen Data Scientist Enablement
    • 4.2.4 Drag-And-Drop AI Workflows
    • 4.2.5 Pre-Built Ml Templates

5. No-Code Machine Learning Market Analysis Of End Use Industries

  • 5.1 Banking, Financial Services And Insurance (Bfsi)
  • 5.2 Healthcare
  • 5.3 Retail
  • 5.4 Information Technology (It) And Telecom
  • 5.5 Manufacturing

6. No-Code Machine Learning Market - Macro Economic Scenario Including The Impact Of Interest Rates, Inflation, Geopolitics, Trade Wars and Tariffs, Supply Chain Impact from Tariff War & Trade Protectionism, And Covid And Recovery On The Market

7. Global No-Code Machine Learning Strategic Analysis Framework, Current Market Size, Market Comparisons And Growth Rate Analysis

  • 7.1. Global No-Code Machine Learning PESTEL Analysis (Political, Social, Technological, Environmental and Legal Factors, Drivers and Restraints)
  • 7.2. Global No-Code Machine Learning Market Size, Comparisons And Growth Rate Analysis
  • 7.3. Global No-Code Machine Learning Historic Market Size and Growth, 2020 - 2025, Value ($ Billion)
  • 7.4. Global No-Code Machine Learning Forecast Market Size and Growth, 2025 - 2030, 2035F, Value ($ Billion)

8. Global No-Code Machine Learning Total Addressable Market (TAM) Analysis for the Market

  • 8.1. Definition and Scope of Total Addressable Market (TAM)
  • 8.2. Methodology and Assumptions
  • 8.3. Global Total Addressable Market (TAM) Estimation
  • 8.4. TAM vs. Current Market Size Analysis
  • 8.5. Strategic Insights and Growth Opportunities from TAM Analysis

9. No-Code Machine Learning Market Segmentation

  • 9.1. Global No-Code Machine Learning Market, Segmentation By Offering, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion
  • Platform, Services
  • 9.2. Global No-Code Machine Learning Market, Segmentation By Deployment Mode, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion
  • Cloud-Based, On-Premise
  • 9.3. Global No-Code Machine Learning Market, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion
  • Banking, Financial Services And Insurance (BFSI), Healthcare, Retail, Information Technology(IT) And Telecom, Manufacturing, Government
  • 9.4. Global No-Code Machine Learning Market, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion
  • Predictive Analytics, Process Automation, Data Visualization, Business Intelligence, Customer Relationship Management, Supply Chain Optimization
  • 9.5. Global No-Code Machine Learning Market, Sub-Segmentation Of Platform, By Type, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion
  • 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
  • 9.6. Global No-Code Machine Learning Market, Sub-Segmentation Of Services, By Type, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion
  • Consulting Services, Implementation Services, Training and Education Services, Support And Maintenance Services, Custom Solution Development Services

10. No-Code Machine Learning Market, Industry Metrics By Country

  • 10.1. Global No-Code Machine Learning Market, Average Selling Price By Country, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $
  • 10.2. Global No-Code Machine Learning Market, Average Spending Per Capita (Employed) By Country, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $

11. No-Code Machine Learning Market Regional And Country Analysis

  • 11.1. Global No-Code Machine Learning Market, Split By Region, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion
  • 11.2. Global No-Code Machine Learning Market, Split By Country, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

12. Asia-Pacific No-Code Machine Learning Market

  • 12.1. Asia-Pacific No-Code Machine Learning Market Overview
  • Region Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 12.2. Asia-Pacific No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

13. China No-Code Machine Learning Market

  • 13.1. China No-Code Machine Learning Market Overview
  • Country Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 13.2. China No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

14. India No-Code Machine Learning Market

  • 14.1. India No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

15. Japan No-Code Machine Learning Market

  • 15.1. Japan No-Code Machine Learning Market Overview
  • Country Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 15.2. Japan No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

16. Australia No-Code Machine Learning Market

  • 16.1. Australia No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

17. Indonesia No-Code Machine Learning Market

  • 17.1. Indonesia No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

18. South Korea No-Code Machine Learning Market

  • 18.1. South Korea No-Code Machine Learning Market Overview
  • Country Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 18.2. South Korea No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

19. Taiwan No-Code Machine Learning Market

  • 19.1. Taiwan No-Code Machine Learning Market Overview
  • Country Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 19.2. Taiwan No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

20. South East Asia No-Code Machine Learning Market

  • 20.1. South East Asia No-Code Machine Learning Market Overview
  • Region Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 20.2. South East Asia No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

21. Western Europe No-Code Machine Learning Market

  • 21.1. Western Europe No-Code Machine Learning Market Overview
  • Region Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 21.2. Western Europe No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

22. UK No-Code Machine Learning Market

  • 22.1. UK No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

23. Germany No-Code Machine Learning Market

  • 23.1. Germany No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

24. France No-Code Machine Learning Market

  • 24.1. France No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

25. Italy No-Code Machine Learning Market

  • 25.1. Italy No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

26. Spain No-Code Machine Learning Market

  • 26.1. Spain No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

27. Eastern Europe No-Code Machine Learning Market

  • 27.1. Eastern Europe No-Code Machine Learning Market Overview
  • Region Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 27.2. Eastern Europe No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

28. Russia No-Code Machine Learning Market

  • 28.1. Russia No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

29. North America No-Code Machine Learning Market

  • 29.1. North America No-Code Machine Learning Market Overview
  • Region Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 29.2. North America No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

30. USA No-Code Machine Learning Market

  • 30.1. USA No-Code Machine Learning Market Overview
  • Country Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 30.2. USA No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

31. Canada No-Code Machine Learning Market

  • 31.1. Canada No-Code Machine Learning Market Overview
  • Country Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 31.2. Canada No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

32. South America No-Code Machine Learning Market

  • 32.1. South America No-Code Machine Learning Market Overview
  • Region Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 32.2. South America No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

33. Brazil No-Code Machine Learning Market

  • 33.1. Brazil No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

34. Middle East No-Code Machine Learning Market

  • 34.1. Middle East No-Code Machine Learning Market Overview
  • Region Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 34.2. Middle East No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

35. Africa No-Code Machine Learning Market

  • 35.1. Africa No-Code Machine Learning Market Overview
  • Region Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 35.2. Africa No-Code Machine Learning Market, Segmentation By Offering, Segmentation By Deployment Mode, Segmentation By Industry Vertical, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

36. No-Code Machine Learning Market Regulatory and Investment Landscape

37. No-Code Machine Learning Market Competitive Landscape And Company Profiles

  • 37.1. No-Code Machine Learning Market Competitive Landscape And Market Share 2024
    • 37.1.1. Top 10 Companies (Ranked by revenue/share)
  • 37.2. No-Code Machine Learning Market - Company Scoring Matrix
    • 37.2.1. Market Revenues
    • 37.2.2. Product Innovation Score
    • 37.2.3. Brand Recognition
  • 37.3. No-Code Machine Learning Market Company Profiles
    • 37.3.1. Apple Create ML Overview, Products and Services, Strategy and Financial Analysis
    • 37.3.2. Microsoft Azure Machine Learning Studio Overview, Products and Services, Strategy and Financial Analysis
    • 37.3.3. Amazon Web Services Overview, Products and Services, Strategy and Financial Analysis
    • 37.3.4. SAS Viya Overview, Products and Services, Strategy and Financial Analysis
    • 37.3.5. DataRobot Inc Overview, Products and Services, Strategy and Financial Analysis

38. No-Code Machine Learning Market Other Major And Innovative Companies

  • 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

39. Global No-Code Machine Learning Market Competitive Benchmarking And Dashboard

40. Key Mergers And Acquisitions In The No-Code Machine Learning Market

41. No-Code Machine Learning Market High Potential Countries, Segments and Strategies

  • 41.1. No-Code Machine Learning Market In 2030 - Countries Offering Most New Opportunities
  • 41.2. No-Code Machine Learning Market In 2030 - Segments Offering Most New Opportunities
  • 41.3. No-Code Machine Learning Market In 2030 - Growth Strategies
    • 41.3.1. Market Trend Based Strategies
    • 41.3.2. Competitor Strategies

42. Appendix

  • 42.1. Abbreviations
  • 42.2. Currencies
  • 42.3. Historic And Forecast Inflation Rates
  • 42.4. Research Inquiries
  • 42.5. The Business Research Company
  • 42.6. Copyright And Disclaimer