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自動機器學習 (AutoML) 市場 - 2023 年至 2028 年預測
市場調查報告書
商品編碼
1302956

自動機器學習 (AutoML) 市場 - 2023 年至 2028 年預測

Automated Machine Learning (AUTOML) Market - Forecasts from 2023 to 2028
出版日期: | 出版商: Knowledge Sourcing Intelligence | 英文 138 Pages | 商品交期: 最快1-2個工作天內

價格
簡介目錄

預計2021年自動化機器學習市場規模將達到653,805,000美元，複合年增長率為44.14%，到2028年將達到8,450,981,000美元。

自動化機器學習 (AutoML) 是使用人工智能 (AI) 算法自動構建、優化和部署機器學習模型的過程。這項技術允許公司以最少的人為乾預自動構建預測模型。對AutoML 產品的需求不斷增長，因為那些無法充分接觸數據科學家或有限的機器學習專業知識的公司可以更好地了解其客戶、產品和其他關鍵業務指標。這要歸功於AutoML 在創建準確模型以快速做出預測方面的足智多謀和實用性。容易地。AutoML 通過同時設計優化模型性能所需的特徵工程和超參數調整，自動為給定任務選擇最佳 ML 算法。此外，您可以自動化模型部署和擴展以支持生產用例。AutoML 市場的增長是由於對速度、效率和準確性更高的機器學習解決方案的需求，以及當前數據科學專家的短缺以及整個行業越來越多地採用人工智能和雲服務，從而推動了AutoML 市場的增長。並表示將同步推進。

對公司特定數據分析和預測的需求不斷增加。

企業生成和收集的數據量的巨大增加增加了對數據分析和預測模型的需求。AutoML解決方案幫助企業快速、高效、準確地處理這些數據，使他們能夠從數據中獲得有價值的見解，從而為AutoML市場擴張創造機會。例如，PayPal 報告稱，通過採用 H2O.ai 的 AutoML 工具，其欺詐檢測模型的效率從 89% 提高到 94.7%。此外，採用DataRobot AutoML軟件後，Lenovo的銷售預測模型準確率提高了7.5%。此外，床上用品解決方案提供商 California Design Den 使用 Google 的 AutoML 工具將庫存結轉減少了約 50%。

AutoML 解決方案的高成本和有限的定制仍然是一個主要挑戰。

AutoML 解決方案價格昂貴，這可能會限制採用，因為各種小型企業和企業需要權衡使用 AutoML 解決方案的收益和成本，以確保他們獲得良好的投資回報。有性別之分。此外，AutoML解決方案在構建機器學習模型所涉及的自動化方面的定制非常有限，並且很難將非定制的AutoML解決方案與現有業務應用程序和工作流程集成，因此需要定制AutoML解決方案的企業的有效採用可能會受到限制。

從供應商來看，科技巨頭佔據了 AutoML 市場的最大份額。

Google、Amazon和Microsoft等科技巨頭認識到對早期自動化機器學習解決方案不斷增長的需求，並大力投資 AutoML 解決方案。例如，Microsoft Azure 提供基於雲的 AutoML 解決方案，允許企業構建自定義機器學習模型，而無需廣泛的技術專業知識，並在 ML 模型上執行特徵工程、算法選擇、超參數調整等各種功能。自動化一些施工中涉及的任務。此外，這些公司提供的軟件平台效率的提高正在增加 AutoML 平台的消耗。

欺詐檢測領域預計將佔據自動化機器學習市場的大部分應用份額。

AutoML 被廣泛用於通過數據準備、模型選擇、超參數、集成技術等來構建 BFSI 和電子商務等各個行業中的欺詐檢測預測模型。AutoML 執行數據清理和預處理任務，例如數據插補、縮放和特徵工程，以確保用於欺詐檢測的數據準確且一致。此外，通過調整 ML 模型的超參數並優化其在給定數據集上的性能，您可以確保欺詐檢測模型穩健並且可以很好地推廣到新數據。BFSI 運營的各種電子商務網站和公司的在線欺詐事件數量不斷增加，對欺詐檢測解決方案和模型產生了很高的需求，預計這將在預測期內增加該細分市場的市場份額。例如，2021 年 3 月，印度一家大型銀行公司發生的欺詐事件金額達 4.92 萬億盧比。印度法醫研究顯示，2022 年 9 月增加了 3634.2 億盧比。此外，領先的交易和支付服務公司 Worldline SA 估計，支付欺詐造成的損失相當於 2022 年電子商務供應商總銷售額的 3.6%。

亞太地區佔自動化機器學習市場的大部分，預計在預測期內將增長。

該地區零售和電子商務領域的快速發展預計將推動 AutoML 市場的增長。AutoML解決方案在零售行業被廣泛採用，用於分析客戶數據，構建需求預測、客戶細分和個性化營銷的預測模型，以改善零售商的客戶體驗並增加銷售額。我們正在支持。

市場趨勢：

  • 2023 年 3 月，TDK 公司新收購的 Qeexo 推出了 Arm Keil，這是一個基於 Arm(R) 架構的微控制器編程平台，採用設計、構建和測試嵌入式應用程序所需的各種工具。 MDK的新集成自動化ML 平台。
  • 2022年10月，通過其雲平台提供合規和安全服務的Qualys Inc.宣布收購雲安全公司Blue Hexagon的AutoML和AI軟件，為使用Qualys雲平台的消費者提供數據集成和AI軟件，提供數據洞察能力。
  • 2021 年 9 月，生產自動化機器學習技術的公司 Big Squid 被 Qlik Technologies 收購。該公司提供集成和數據分析服務，通過集成 AutoML 和 AI 技術來增強 Qlik Technologies 提供的預測分析解決方案。

目錄

第 1 章 簡介

  • 市場概況
  • 市場定義
  • 調查範圍
  • 市場細分
  • 貨幣
  • 先決條件
  • 基準年和預測年的時間表

第二章研究方法論

  • 調查數據
  • 調查過程

第三章執行摘要

  • 調查亮點

第四章市場動態

  • 市場驅動力
  • 市場製約因素
  • 波特五力分析
  • 行業價值鏈分析

5. 按提供商劃分的自動化機器學習 (AutoML) 市場

  • 介紹
  • 開源
  • 啟動
  • 科技巨頭

第 6 章 自動機器學習 (AutoML) 市場：按應用分類

  • 介紹
  • 欺詐識別
  • AML檢測
  • 價錢
  • 營銷和銷售管理
  • 其他

第 7 章 自動機器學習 (AutoML) 市場：按地區

  • 介紹
  • 美洲
    • 美國
    • 其他
  • 歐洲、中東/非洲
    • 德國
    • 法國
    • 英國
    • 其他
  • 亞太地區
    • 中國
    • 日本
    • 韓國
    • 其他

第八章競爭格局與分析

  • 主要公司及戰略分析
  • 初創企業和市場盈利能力
  • 合併、收購、協議與合作
  • 供應商競爭力矩陣

第九章公司簡介

  • IBM
  • Microsoft Corporation
  • Amazon Web Services
  • Oracle
  • Alphabet Inc.（Google）
  • Databricks
  • Qlik
  • Akkio Inc.
  • Obviously AI, Inc.
簡介目錄
Product Code: KSI061615199

The automated machine learning market size was valued at US$653.805 million in 2021 and is expected to grow at a CAGR of 44.14% to reach US$8,450.981 million by 2028.

Automated Machine Learning (AutoML) is a process of using Artificial Intelligence (AI) algorithms to automate the process of building, optimizing, and deploying machine learning models. It is a technology that enables businesses to build predictive models with minimal human intervention automatically. The rising demand for autoML products can be attributed to the resourcefulness and usefulness of autoML in creating accurate models to make better predictions about customers, products, or other important business metrics quickly and easily for businesses that do not have proper access to data scientists or have limited expertise in machine learning to. AutoML works by automating the selection of the best ML algorithms for a given task by simultaneously designing the feature engineering and hyperparameter tuning required to optimize model performance. In addition, it can automate the deployment and scaling of models to support production use cases. The growth of the AutoML market is expected to be driven by the need for machine learning solutions with enhanced speed, efficiency, and accuracy, combined with the existing shortage of data science experts and the increasing adoption of AI and cloud services across industries.

Increasing need for data analysis and prediction by companies.

The massive increase in the amount of data generated and collected by companies is growing the demand for data analysis and prediction models, which is creating an opportunity for the expansion of the autoML market as AutoML solutions help companies to process this data quickly, efficiently and accurately, enabling them to extract valuable insights from their data. For instance, PayPal company reported that the efficiency of its fraud detection model increased from 89% to 94.7% through the adoption of H2O.ai's AutoML tool. In addition, the sales prediction model of Lenovo company witnessed an increase in accuracy by 7.5% after the adoption of autoML software by DataRobot Company. Further, California Design Den, a company providing bedding solutions, lowered its inventory carryover by approximately 50% by using the autoML tool offered by Google.

The high cost and limited customization of autoML solutions remain a significant challenge.

AutoML solutions are expensive, which could restrain their adoption by various small and medium-sized firms and businesses as they need to weigh the benefits of using AutoML solutions against the cost to ensure that the return on investment is sufficient. Further, AutoML solutions are highly limited in customization in automating involved in building machine learning models, which limits their adoption by businesses that require effectively customized AutoML solutions since integrating non-customized AutoML solutions with existing business applications and workflows can be challenging.

By provider, the tech-giants sector holds the most significant portion of the autoML market.

Tech giants like Google, Amazon, and Microsoft have invested heavily in AutoML solutions by recognizing the growing demand for automated ML solutions at the initial stage. For instance, Microsoft Azure offers a cloud-based AutoML solution that enables businesses to build custom machine learning models without requiring extensive technical expertise to automate several tasks involved in building ML models, including feature engineering, algorithm selection, and hyperparameter tuning. Further, the increase in the efficiency of the software platforms offered by these companies is increasing the consumption of their autoML platforms.

The fraud detection segment is expected to have a major share of the automated machine learning market by application.

AutoML is extensively adopted to build predictive models for fraud detection in different industries, such as the BFSI and e-commerce, through data preparation, model selection, hyperparameter, and ensemble methods. AutoML performs data cleaning and preprocessing tasks such as data imputation, scaling, and feature engineering, ensuring that the data used for fraud detection is accurate and consistent. In addition, it can tune the hyperparameters of ML models to optimize their performance on a given dataset to ensure that the fraud detection models are robust and can generalize appropriately to new data. The rising incidents of online fraud in various e-commerce sites and companies operating in the BFSI are expected to increase the market share of this sector over the forecast period as it is generating a high demand for fraud detection solutions and models. For instance, fraud incidents among major banking companies in India in March 2021 amounted to Rs.4.92 trillion. It increased by Rs. 36342 crores during September 2022, as per research conducted by Indiaforensic. In addition, Worldline SA, a leading company offering transaction and payment services, estimated that payment fraud created a loss of 3.6% of the total sales made by e-commerce vendors in 2022.

Asia Pacific region holds a significant portion of the auto machine learning market and is expected to grow in the forecast period.

The rapid advancement of the retail and e-commerce sector in the region is expected to promote the growth of the autoML market as AutoML solutions are being extensively adopted in the retail industry to build predictive models for demand forecasting, customer segmentation, and personalized marketing by analyzing customer data to help retailers improve customer experiences and increase sales.

Market Developments:

  • In March 2023, a newly acquired company by TDK Corporation, Qeexo, released a new integrated auto ML platform for Arm Keil MDK, a programming platform for microcontrollers based on the Arm® architecture adopted with different tools required to design, construct, and test embedded applications.
  • In October 2022, Qualys Inc., a company providing compliance and security services through its cloud platform, declared the acquisition of autoML and AI software of Blue Hexagon, a cloud security company, to provide data integration and data insight features to consumers using Qualys Cloud Platform.
  • In September 2021, Big Squid, a company producing automated ML technology, was acquired by Qlik Technologies. This company offers integration and data analytics services to enhance the predictive analysis solution offered by Qlik Technologies by integrating autoML and AI technology.

Market Segmentation:

By Provider

  • Open Source
  • Startups
  • Tech Giants

By Application

  • Fraud Detection
  • AML Detection
  • Pricing
  • Marketing and Sales Management
  • Others

By Geography

  • Americas
  • USA
  • Others
  • Europe Middle East and Africa
  • Germany
  • France
  • United Kingdom
  • Others
  • Asia Pacific
  • China
  • Japan
  • South Korea
  • Others

TABLE OF CONTENTS

1. INTRODUCTION

  • 1.1. Market Overview
  • 1.2. Market Definition
  • 1.3. Scope of the Study
  • 1.4. Market Segmentation
  • 1.5. Currency
  • 1.6. Assumptions
  • 1.7. Base, and Forecast Years Timeline

2. RESEARCH METHODOLOGY

  • 2.1. Research Data
  • 2.2. Research Process

3. EXECUTIVE SUMMARY

  • 3.1. Research Highlights

4. MARKET DYNAMICS

  • 4.1. Market Drivers
  • 4.2. Market Restraints
  • 4.3. Porter's Five Force Analysis
    • 4.3.1. Bargaining Power of Suppliers
    • 4.3.2. Bargaining Power of Buyers
    • 4.3.3. Threat of New Entrants
    • 4.3.4. Threat of Substitutes
    • 4.3.5. Competitive Rivalry in the Industry
  • 4.4. Industry Value Chain Analysis

5. AUTOMATED MACHINE LEARNING (AUTOML) MARKET, BY PROVIDER

  • 5.1. Introduction
  • 5.2. Open Source
  • 5.3. Startups
  • 5.4. Tech Giants

6. AUTOMATED MACHINE LEARNING (AUTOML) MARKET, BY APPLICATION

  • 6.1. Introduction
  • 6.2. Fraud Detection
  • 6.3. AML Detection
  • 6.4. Pricing
  • 6.5. Marketing and Sales Management
  • 6.6. Others

7. AUTOMATED MACHINE LEARNING (AUTOML) MARKET, BY GEOGRAPHY

  • 7.1. Introduction
  • 7.2. Americas
    • 7.2.1. USA
    • 7.2.2. Others
  • 7.3. Europe, Middle East and Africa
    • 7.3.1. Germany
    • 7.3.2. France
    • 7.3.3. United Kingdom
    • 7.3.4. Others
  • 7.4. Asia Pacific
    • 7.4.1. China
    • 7.4.2. Japan
    • 7.4.3. South Korea
    • 7.4.4. Others

8. COMPETITIVE ENVIRONMENT AND ANALYSIS

  • 8.1. Major Players and Strategy Analysis
  • 8.2. Emerging Players and Market Lucrativeness
  • 8.3. Mergers, Acquisitions, Agreements, and Collaborations
  • 8.4. Vendor Competitiveness Matrix

9. COMPANY PROFILES

  • 9.1. IBM
  • 9.2. Microsoft Corporation
  • 9.3. Amazon Web Services
  • 9.4. Oracle
  • 9.5. Alphabet Inc. (Google)
  • 9.6. Databricks
  • 9.7. Qlik
  • 9.8. Akkio Inc.
  • 9.9. Obviously AI, Inc.