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市場調査レポート
商品コード
1724880
サービスとしての機械学習の市場規模、シェア、成長分析、コンポーネント別、組織規模別、用途別、エンドユーザー別、地域別 - 産業予測 2025-2032年Machine Learning as a Service Market Size, Share, and Growth Analysis, By Component (Solution, Services), By Organization Size (Small and Medium-Sized Enterprises, Large Enterprises), By Application, By End User, By Region - Industry Forecast 2025-2032 |
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サービスとしての機械学習の市場規模、シェア、成長分析、コンポーネント別、組織規模別、用途別、エンドユーザー別、地域別 - 産業予測 2025-2032年 |
出版日: 2025年05月11日
発行: SkyQuest
ページ情報: 英文 189 Pages
納期: 3~5営業日
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サービスとしての機械学習(MLaaS)の世界市場規模は、2023年に409億米ドルと評価され、2024年の568億9,000万米ドルから2032年には7,973億8,000万米ドルに成長し、予測期間(2025年~2032年)のCAGRは39.1%で成長する見通しです。
クラウドコンピューティングの急速な普及が、MLaaS業界の成長を大きく後押ししています。MLaaSは、スケーラブルで費用対効果の高いAIソリューションを提供することで、企業は高価なインフラや専門スキルの負担なしに複雑な機械学習モデルを導入することができます。主要なクラウドプロバイダーは、学習済みモデル、API、自動化ツールで常にサービスを強化し、開発プロセスを合理化しています。デジタルトランスフォーメーションが加速する中、ヘルスケアや金融など様々な分野の企業が、特に予測分析において、MLaaSを活用して業務を自動化し、データ主導の意思決定を行っています。この機能により、企業は膨大な量のデータを分析し、顧客行動、リスク管理、パーソナライズされたマーケティングに関する洞察を得ることができ、効率性の向上、リスクの低減、成長機会の創出を促進し、MLaaSの需要を高めています。
Global Machine Learning as a Service Market size was valued at USD 40.9 billion in 2023 and is poised to grow from USD 56.89 billion in 2024 to USD 797.38 billion by 2032, growing at a CAGR of 39.1% during the forecast period (2025-2032).
The rapid adoption of cloud computing has significantly fueled the growth of the machine learning as a service (MLaaS) industry. By providing scalable and cost-effective AI solutions, MLaaS enables companies to deploy complex machine learning models without the burden of expensive infrastructure or specialized skills. Major cloud providers constantly enhance their offerings with pre-trained models, APIs, and automation tools, streamlining the development process. As digital transformation accelerates, businesses across various sectors, including healthcare and finance, are leveraging MLaaS to automate operations and make data-driven decisions, particularly in predictive analytics. This capability allows companies to analyze vast amounts of data for insights on customer behavior, risk management, and personalized marketing, driving increased efficiency, reducing risks, and creating growth opportunities, thereby boosting demand for MLaaS.
Top-down and bottom-up approaches were used to estimate and validate the size of the Global Machine Learning as a Service 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 Machine Learning as a Service Market Segments Analysis
Global Machine Learning as a Service Market is segmented by Component, Organization Size, Application, End User and region. Based on Component, the market is segmented into Solution and Services. Based on Organization Size, the market is segmented into Small and Medium-Sized Enterprises and Large Enterprises. Based on Application, the market is segmented into Marketing & Advertising, Fraud Detection & Risk Management, Computer vision, Security & Surveillance, Predictive analytics, Natural Language Processing, Augmented & Virtual Reality and Others. Based on End User, the market is segmented into BFSI, IT & Telecom, Automotive, Healthcare, Aerospace & Defense, Retail, Government and Others. Based on region, the market is segmented into North America, Europe, Asia Pacific, Latin America and Middle East & Africa.
Driver of the Global Machine Learning as a Service Market
The primary catalyst for the expansion of the global machine learning as a service (MLaaS) market is the increasing adoption of cloud technologies. The advantages provided by cloud platforms, such as scalable infrastructure, significant cost reductions, and ease of integration, are pivotal in propelling this growth. Moreover, MLaaS facilitates real-time analytics, automation, and predictive modeling, which are being embraced across various industries. This adoption not only fosters digital transformation but also enhances the accessibility of artificial intelligence, further accelerating the momentum of the MLaaS market on a global scale.
Restraints in the Global Machine Learning as a Service Market
A significant hurdle hindering the adoption of Machine Learning as a Service (MLaaS) is the opaque "black-box" characteristic of machine learning models, which complicates the interpretation of their decisions. This lack of transparency poses a concern, especially for industries such as finance and healthcare, where stakeholders are hesitant to fully trust AI-driven insights due to potential biases and the difficulty in understanding the rationale behind these conclusions. Consequently, this apprehension leads to a reluctance in integrating MLaaS into essential decision-making processes, ultimately impeding its widespread use in critical applications.
Market Trends of the Global Machine Learning as a Service Market
The Global Machine Learning as a Service (MLaaS) market is witnessing a significant transformation, driven by the increasing demand for no-code and low-code ML solutions that empower organizations to implement AI models effortlessly. Major vendors like Google Cloud AI and Microsoft Azure are enhancing their user-friendly platforms, facilitating AI adoption across various sectors. This trend reflects a broader shift towards democratizing machine learning technology, as businesses seek to leverage AI capabilities without requiring extensive programming skills. Consequently, the market is expected to grow robustly, as reduced technical barriers and faster deployment times accelerate the integration of AI into diverse applications.