無程式碼機器學習市場規模、佔有率和成長分析：按組件、部署類型、應用、企業規模、最終用戶和地區分類－2026-2033年產業預測

2024 年全球無程式碼機器學習市場價值為 148.2 億美元，預計到 2025 年將成長至 190.3 億美元，到 2033 年將成長至 1405.9 億美元，在預測期（2026-2033 年）內複合年成長率為 28.4%。

全球無程式碼機器學習市場正透過革新企業建構、訓練和部署預測模型的方式，有效地普及資料科學，使用戶無需具備程式設計技能即可參與其中。這一轉變不僅緩解了人工智慧人才短缺的困境，同時滿足了日益成長的分析需求，也使業務分析師能夠主導專案。雲端服務和訂閱模式降低了進入門檻，使得零售和醫療保健等行業能夠更快地建立模型原型製作。此外，隨著監管壓力要求人工智慧決策更加透明，模型可解釋性工具的整合至關重要。透過提供視覺化解釋和偏差診斷，企業可以向相關人員解釋其決策背後的邏輯。隨著人工智慧驅動的自動化透過直覺的介面簡化模型創建，使用者群體不斷擴大，推動著這個充滿活力的市場持續發展和創新。

全球無程式碼機器學習市場促進因素

全球無程式碼機器學習市場的主要驅動力在於，企業無需具備高階編碼技能即可創建和部署機器學習解決方案，從而促進快速原型製作和迭代開發。這種使用者友善方法使跨職能團隊能夠獨立應對分析挑戰，最大限度地減少對專業開發人員的依賴，並加快價值實現速度。因此，業務部門可以更頻繁地試驗預測模型，從而激發創新並推動對無程式碼平台的需求。此外，簡化的工作流程增強了資料科學家和領域專家之間的協作，確保模型假設與業務目標保持一致，並提高對最終解決方案的信心。

全球無程式碼機器學習市場面臨的限制因素

由於無程式碼機器學習模型的決策流程，許多公司對無程式碼機器學習市場持謹慎態度。這種推理過程缺乏透明度會削弱相關人員對結果的信心，從而引發監管合規性的擔憂，尤其是在金融和醫療保健等敏感產業。因此，企業可能會延遲或限制採用無程式碼解決方案，轉而選擇模型可解釋性更強的傳統方法。此外，追蹤特徵貢獻的挑戰導致審計追蹤不足，使得在需要嚴格管治的環境中，企業對採用無程式碼機器學習猶豫不決。這些顧慮最終阻礙了無程式碼機器學習技術的廣泛應用。

全球無程式碼機器學習市場趨勢

隨著企業紛紛採用人工智慧驅動的自助式分析，全球無程式碼機器學習市場正經歷一場變革，這使得業務用戶能夠設計、訓練和評估自己的機器學習模型。這種向方便用戶使用型介面的轉變，以及對傳統編碼依賴的減少，正在普及高級分析，即使是非技術人員也能利用整合到工作流程中的預測洞察，并快速響應市場動態。隨著供應商不斷增強其平台功能，例如預訓練模型庫和自動化部署流程，全球各地的組織都在努力建立一種將分析融入每個部門的文化，從而提升企業敏捷性和決策的準確性。

目錄

介紹

• 調查目的
• 市場定義和範圍

調查方法

• 研究過程
• 二級資料和一級資料的方法
• 市場規模估算方法

執行摘要

• 全球市場展望
• 市場主要亮點
• 細分市場概覽
• 競爭環境概述

市場動態及展望

• 總體經濟指標
• 促進者和機會
• 抑制因素和挑戰
• 供給面趨勢
• 需求面趨勢
• 波特的分析和影響

關鍵市場分析

• 關鍵成功因素
• 影響市場的因素
• 主要投資機會
• 生態系測繪
• 2025年市場魅力指數
• PESTLE分析
• 監理情勢

全球無程式碼機器學習市場規模：按組件分類

• 平台
• 服務

全球無程式碼機器學習市場規模：以部署方式分類

• 基於雲端的
• 現場

全球無程式碼機器學習市場規模：按應用領域分類

• 預測分析
• 電腦視覺
• 自然語言處理
• 建議​​統

全球無程式碼機器學習市場規模：依公司規模分類

• 大公司
• 小型企業

全球無程式碼機器學習市場規模：按最終用戶分類

• BFSI
• 衛生保健
• 零售
• 製造業

全球無程式碼機器學習市場規模：按地區分類

• 北美洲
  • 美國
  • 加拿大
• 歐洲
  • 德國
  • 西班牙
  • 法國
  • 英國
  • 義大利
  • 其他歐洲國家
• 亞太地區
  • 中國
  • 印度
  • 日本
  • 韓國
  • 其他亞太國家
• 拉丁美洲
  • 墨西哥
  • 巴西
  • 其他拉丁美洲國家
• 中東和非洲
  • 海灣合作理事會國家
  • 南非
  • 其他中東和非洲國家

競爭資訊

• 前五大公司對比
• 主要公司2025年的市場定位
• 主要市場公司採取的策略
• 近期市場趨勢
• 企業市場占有率分析，2025 年
• 主要公司的完整公司簡介
  • 公司詳情
  • 產品系列分析
  • 按細分市場進行企業市佔率分析
  • 銷售收入年比比較（2023-2025 年）

主要公司簡介

• Microsoft Corporation
• Google LLC
• Amazon Web Services, Inc.
• IBM Corporation
• DataRobot, Inc.
• Dataiku SAS
• H2O.ai, Inc.
• Alteryx, Inc.
• Salesforce, Inc.
• SAP SE
• Oracle Corporation
• Akkio Inc.
• Obviously AI
• Levity AI GmbH
• RapidMiner, Inc.
• KNIME AG
• Pecan AI Ltd.
• QlikTech International AB
• Zoho Corporation
• Aible, Inc.

結論與建議

Global No-Code Machine Learning Market size was valued at USD 14.82 Billion in 2024 and is poised to grow from USD 19.03 Billion in 2025 to USD 140.59 Billion by 2033, growing at a CAGR of 28.4% during the forecast period (2026-2033).

The global no-code machine learning market is revolutionizing how enterprises build, train, and deploy predictive models without requiring coding skills, effectively democratizing data science. This shift addresses the shortage of AI talent while meeting the growing demand for analytics, empowering business analysts to spearhead projects. Cloud services and subscription models have lowered barriers to entry, enabling quicker prototyping of models across industries such as retail and healthcare. The integration of model-explainability tools is also crucial, as regulatory pressures demand transparency in AI decision-making. By providing visual explanations and bias diagnostics, organizations can justify choices to stakeholders. As AI-driven automation simplifies model creation through intuitive interfaces, the user base expands, fueling continued adoption and innovation in this dynamic market.

Top-down and bottom-up approaches were used to estimate and validate the size of the Global No-Code Machine Learning market and to estimate the size of various other dependent submarkets. The research methodology used to estimate the market size includes the following details: The key players in the market were identified through secondary research, and their market shares in the respective regions were determined through primary and secondary research. This entire procedure includes the study of the annual and financial reports of the top market players and extensive interviews for key insights from industry leaders such as CEOs, VPs, directors, and marketing executives. All percentage shares split, and breakdowns were determined using secondary sources and verified through Primary sources. All possible parameters that affect the markets covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data.

Global No-Code Machine Learning Market Segments Analysis

Global no-code machine learning market is segmented by component, deployment, application, enterprise size, end user and region. Based on component, the market is segmented into Platforms and Services. Based on deployment, the market is segmented into Cloud-Based and On-Premises. Based on application, the market is segmented into Predictive Analytics, Computer Vision, Natural Language Processing and Recommendation Systems. Based on enterprise size, the market is segmented into Large Enterprises and Small & Medium Enterprises. Based on end user, the market is segmented into BFSI, Healthcare, Retail and Manufacturing. Based on region, the market is segmented into North America, Europe, Asia Pacific, Latin America and Middle East & Africa.

Driver of the Global No-Code Machine Learning Market

The Global No-Code Machine Learning market is significantly driven by the ability of organizations to create and deploy machine learning solutions without needing extensive coding skills, facilitating rapid prototyping and iteration. This user-friendly approach allows cross-functional teams to tackle analytical problems independently, minimizing dependence on specialized developers and accelerating the time it takes to realize value. Consequently, business units can more frequently experiment with predictive models, stimulating innovation and boosting demand for no-code platforms. Furthermore, the simplified workflow enhances collaboration between data scientists and domain experts, aligning model assumptions with business goals and bolstering confidence in the solutions produced.

Restraints in the Global No-Code Machine Learning Market

Numerous businesses exercise caution towards the no-code machine learning market due to the often opaque decision-making processes associated with these models. This lack of transparent reasoning can erode stakeholder trust in the outcomes generated, leading to concerns about regulatory compliance, especially in sensitive sectors like finance and healthcare. Consequently, organizations might opt to delay or restrict the implementation of no-code solutions, favoring more conventional methods that provide clearer model explainability. Furthermore, the challenges in tracing feature contributions create weak audit trails, which can dissuade adoption in environments that demand strict governance. This hesitation ultimately hampers the broader acceptance of no-code machine learning technologies.

Market Trends of the Global No-Code Machine Learning Market

The Global No-Code Machine Learning market is witnessing a transformative trend as enterprises embrace AI-powered self-service analytics, enabling business users to independently design, train, and evaluate machine-learning models. This shift towards user-friendly interfaces, coupled with the reduction of reliance on traditional coding, democratizes access to advanced analytics, allowing non-technical staff to swiftly adapt to market dynamics with predictive insights integrated into their workflows. As vendors enhance their platforms with features like pre-trained model libraries and automated deployment processes, organizations globally are cultivating a culture where analytics is embedded across all functions, fostering agility and informed decision-making throughout the enterprise.

Table of Contents

Introduction

• Objectives of the Study
• Market Definition & Scope

Research Methodology

• Research Process
• Secondary & Primary Data Methods
• Market Size Estimation Methods

Executive Summary

• Global Market Outlook
• Key Market Highlights
• Segmental Overview
• Competition Overview

Market Dynamics & Outlook

• Macro-Economic Indicators
• Drivers & Opportunities
• Restraints & Challenges
• Supply Side Trends
• Demand Side Trends
• Porters Analysis & Impact
  • Competitive Rivalry
  • Threat of Substitute
  • Bargaining Power of Buyers
  • Threat of New Entrants
  • Bargaining Power of Suppliers

Key Market Insights

• Key Success Factors
• Market Impacting Factors
• Top Investment Pockets
• Ecosystem Mapping
• Market Attractiveness Index 2025
• PESTEL Analysis
• Regulatory Landscape

Global No-Code Machine Learning Market Size by Component & CAGR (2026-2033)

• Market Overview
• Platforms
• Services

Global No-Code Machine Learning Market Size by Deployment & CAGR (2026-2033)

• Market Overview
• Cloud-Based
• On-Premises

Global No-Code Machine Learning Market Size by Application & CAGR (2026-2033)

• Market Overview
• Predictive Analytics
• Computer Vision
• Natural Language Processing
• Recommendation Systems

Global No-Code Machine Learning Market Size by Enterprise Size & CAGR (2026-2033)

• Market Overview
• Large Enterprises
• Small & Medium Enterprises

Global No-Code Machine Learning Market Size by End User & CAGR (2026-2033)

• Market Overview
• BFSI
• Healthcare
• Retail
• Manufacturing

Global No-Code Machine Learning Market Size & CAGR (2026-2033)

• North America (Component, Deployment, Application, Enterprise Size, End User)
  • US
  • Canada
• Europe (Component, Deployment, Application, Enterprise Size, End User)
  • Germany
  • Spain
  • France
  • UK
  • Italy
  • Rest of Europe
• Asia Pacific (Component, Deployment, Application, Enterprise Size, End User)
  • China
  • India
  • Japan
  • South Korea
  • Rest of Asia-Pacific
• Latin America (Component, Deployment, Application, Enterprise Size, End User)
  • Mexico
  • Brazil
  • Rest of Latin America
• Middle East & Africa (Component, Deployment, Application, Enterprise Size, End User)
  • GCC Countries
  • South Africa
  • Rest of Middle East & Africa

Competitive Intelligence

• Top 5 Player Comparison
• Market Positioning of Key Players, 2025
• Strategies Adopted by Key Market Players
• Recent Developments in the Market
• Company Market Share Analysis, 2025
• Company Profiles of All Key Players
  • Company Details
  • Product Portfolio Analysis
  • Company's Segmental Share Analysis
  • Revenue Y-O-Y Comparison (2023-2025)

Key Company Profiles

• Microsoft Corporation
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Google LLC
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Amazon Web Services, Inc.
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• IBM Corporation
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• DataRobot, Inc.
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Dataiku SAS
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• H2O.ai, Inc.
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Alteryx, Inc.
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Salesforce, Inc.
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• SAP SE
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Oracle Corporation
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Akkio Inc.
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Obviously AI
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Levity AI GmbH
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• RapidMiner, Inc.
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• KNIME AG
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Pecan AI Ltd.
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• QlikTech International AB
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Zoho Corporation
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Aible, Inc.
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments

Conclusion & Recommendations