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市場調查報告書
商品編碼
1335905
全球機器學習市場規模、佔有率、行業趨勢分析報告:2023~2030年按公司規模(大型企業、中小企業)、組件(服務、軟體、硬體)、最終用途和地區分類的展望和預測Global Machine Learning Market Size, Share & Industry Trends Analysis Report By Enterprise Size (Large Enterprises, and SMEs), By Component (Services, Software, and Hardware), By End-use, By Regional Outlook and Forecast, 2023 - 2030 |
到 2030 年,機器學習市場規模預計將達到 4,084 億美元,預測期內市場年複合成長率率為 36.7%。
根據KBV Cardinal矩陣發布的分析,Google有限責任公司(Alphabet Inc.)和微軟公司是該市場的先驅。 2022 年3 月,Google 與BT 建立合作夥伴關係,以提供卓越的客戶體驗、降低成本和風險、創造更多收入來源,並讓BT 能夠存取數百個新業務用例。透過這樣做,我們鞏固了圍繞開發數位的目標產品和超個人化的客戶參與。 IBM公司、惠普企業公司和英特爾公司等公司是這個市場的主要創新者。
市場成長要素
透過智慧自動化實現業務轉型的需求不斷成長
人們越來越依賴資料來推動決策和營運效率,從而推動了對智慧業務流程的需求。這些流程使用機器學習演算法來自動化決策並簡化業務營運,從而提高生產力和利潤。透過利用 AutoML,企業可以提高效能、降低成本並簡化流程以獲得競爭優勢。此外,事實證明,使用人工智慧的自動化可以顯著提高生產力。透過自動化機器學習模型的創建和部署,自動化市場可以幫助公司實現這些成果。
更快做出決策並節省成本
透過採用 AutoML 解決方案,公司可以節省昂貴的基礎設施投資和僱用專業人員。此外,人工智慧解決方案的快速開發和部署可以透過提高營運效率和增強決策來節省成本。隨著越來越多的公司採用 AutoML 技術,新的使用案例和應用程式將會激增,從而推動創新和市場成長。此外,機器學習的民主化可以幫助公司擴大服務範圍並開拓新市場,從而有可能增加銷售額和市場佔有率。
市場抑制因素
法律和道德問題
機器學習需要大量資料,有時包括資料和個人資訊。出於隱私和安全考慮,個人和組織可能會猶豫是否為機器學習目的提供資料。機器學習 (ML) 的使用必須遵守各種法律和法律規範,包括行業特定規則、消費者保護法和反歧視法。不遵守這些標準可能會導致法律責任、經濟處罰、公眾形象受損和公眾信任喪失。由於機器學習實施過程中可能出現的法律問題,組織可能會感到不確定和警惕。預計這些要素將阻礙未來幾年的市場擴張。
企業規模展望
根據公司規模,市場分為中小企業和大型企業。到 2022 年,大型企業細分市場將佔據市場最大的收入佔有率。大型企業擴大使用雲端基礎的機器學習平台和服務。可擴展且經濟實惠的雲端平台架構使訓練和部署機器學習模型成為可能。機器學習需要大型企業大量的基礎設施支出,因為 Google Cloud AI Platform、Amazon Web Services (AWS) 和 Microsoft Azure Machine Learning 等服務提供預先建置模型、分散式訓練功能和基礎架構管理。我不需要它。
組件展望
根據組件,市場分為服務、軟體和硬體。 2022 年,硬體領域在市場中佔據了重要的收入佔有率。這可能與為機器學習設計的裝備的日益普及有關。具有人工智慧和機器學習功能的專用矽處理器的開發有助於硬體的普及。隨著 SambaNova Systems 等公司生產更強大的處理設備,該市場預計將繼續擴大。
最終用戶的展望
依最終用戶分類,可分為醫療保健、BFSI、零售、廣告/媒體、汽車/運輸、農業、製造業等。 2022年,廣告和媒體領域以最大的收入佔有率主導市場。主要趨勢之一是超個人化,機器學習演算法會檢查大量用戶資料並創建個人化的相關廣告,從而提高參與度和轉換率。目前,人們非常重視利用機器學習來辨識廣告詐騙。
區域展望
從區域來看,我們對北美、歐洲、亞太地區和拉丁美洲地區的市場進行了分析。 2022年,北美地區以最大的收入佔有率引領市場。在北美,機器學習日益成長的社會影響正在引起人們對道德和負責任的人工智慧實踐的關注。在開發機器學習模型和演算法時,組織優先考慮公平、課責和開放。減少偏見,保護隱私,並解決人工智慧應用的道德問題。法律體制、規則和標準正在製定中,以監督機器學習在該領域的適當使用。
The Global Machine Learning Market size is expected to reach $408.4 billion by 2030, rising at a market growth of 36.7% CAGR during the forecast period.
The usage of machine learning has grown widely by retailers to improve customer experiences. Consequently, Retail segment acquired $3,839.1 million revenue in the market in 2022. In order to process large datasets, identify pertinent metrics, recurrent patterns, anomalies, or cause-and-effect relationships among variables, and thus gain a deeper understanding of the dynamics guiding this industry and the contexts where retailers operate, machine learning is used in the retail industry. Machine learning's expansion in the retail sector is fueled by its capacity to improve consumer experiences, streamline processes, and boost revenue.
The major strategies followed by the market participants are Partnerships as the key developmental strategy to keep pace with the changing demands of end users. For instance, In March, 2023, AWS came into collaboration with NVIDIA to jointly build on-demand AI infrastructure intended for training sophisticated large language models (LLMs) and developing generative AI applications. In June, 2023, Microsoft partnered with HCLTech to help businesses leverage generative artificial intelligence and develop joint solutions to allow businesses to achieve better outcomes and improve business transformation.
Based on the Analysis presented in the KBV Cardinal matrix; Google LLC (Alphabet Inc.) and Microsoft Corporation are the forerunners in the Market. In March, 2022, Google entered into a partnership with BT to offer excellent customer experiences, decrease costs, and risks, and create more revenue streams and to enable BT to get access to hundreds of new business use cases to solidify its goals around digital offerings and developing hyper-personalized customer engagement. Companies such as IBM Corporation, Hewlett-Packard enterprise Company and Intel Corporation are some of the key innovators in the Market.
Market Growth Factors
Growing Demand for Transforming Businesses with Intelligent Automation
There is a rising need for intelligent business processes as organizations depend increasingly on data to inform decisions and boost operational effectiveness. These procedures use machine learning algorithms to automate decision-making and streamline corporate operations, which boosts productivity and profits. By utilizing AutoML, companies can increase performance, lower costs, and streamline processes, giving them a competitive advantage. In addition, AI-powered automation has been demonstrated to increase productivity significantly. By automating the creation and deployment of machine learning models, the automated market can assist firms in achieving these outcomes.
Enabling Fast Decision-Making and Saving Costs
Businesses may save the expenses of investing in costly infrastructure and employing specialist people by adopting AutoML solutions. Additionally, by boosting operational effectiveness and enhancing decision-making, AI solutions' quicker development and implementation may lead to cost savings. There will probably be a proliferation of new use cases and applications as more organizations employ AutoML technologies, boosting innovation and market growth. Additionally, the democratization of machine learning may help companies extend their offers and tap into new markets, increasing sales and market share.
Market Restraining Factors
Legal and Ethical Issues
Large volumes of data, sometimes including sensitive and private data, are necessary for machine learning. Individuals and organizations may hesitate to provide their data for ML purposes because of privacy and security concerns. Various legal and regulatory frameworks, including industry-specific rules, consumer protection laws, and anti-discrimination laws, must be complied with while using machine learning (ML). Failure to comply with these criteria may result in legal responsibilities, financial fines, harm to one's image, and a decline in public confidence. Organizations may be unsure and wary because of the possible legal issues of ML deployment. These factors are anticipated to impede market expansion in the ensuing years.
Enterprise Size Outlook
On the basis of enterprise size, the market is segmented into SMEs and large enterprises. In 2022, the large enterprises segment witnessed the largest revenue share in the market. Large enterprises are increasingly using cloud-based machine learning platforms and services. Machine learning model training and deployment are made feasible by cloud platforms' scalable and affordable architecture. Due to the services like Google Cloud AI Platform, Amazon Web Services (AWS), and Microsoft Azure Machine Learning, which provide pre-built models, distributed training capabilities, and infrastructure management, Machine learning does not need big infrastructure expenditures for large businesses.
Component Outlook
Based on components, the market is divided into services, software, and hardware. The hardware segment acquired a substantial revenue share in the market in 2022. It could be connected to the growing popularity of gear designed for machine learning. The development of specialized silicon processors with AI and ML capabilities is fueling hardware adoption. As more powerful processing devices are created by companies like SambaNova Systems, the market is predicted to keep expanding.
End-Use Outlook
By end-user, the market is categorized into healthcare, BFSI, retail, advertising & media, automotive & transportation, agricultural, manufacturing, and others. In 2022, the advertising & media segment dominated the market with the maximum revenue share. One of the major trends is hyper-personalization, in which machine learning algorithms examine vast amounts of user data to create highly relevant and individualized advertisements that increase engagement and conversion rates. A considerable focus is now being placed on employing machine learning to identify ad fraud.
Regional Outlook
Region wise, the market is analyzed across North America, Europe, Asia Pacific, and LAMEA. In 2022, the North America region led the market with the maximum revenue share. In North America, there is a rising focus on moral AI and responsible AI practices due to machine learning's expanding social influence. Fairness, accountability, and openness are prioritized by organizations while developing machine learning models and algorithms. Biases are being lessened, privacy is protected, and ethical issues about AI applications are being addressed. Legislative frameworks, rules, and standards are being created to oversee the proper use of machine learning in the area.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Amazon Web Services, Inc. (Amazon.com, Inc.), Baidu, Inc., Google LLC (Alphabet Inc.), H2O.ai, Inc., Hewlett-Packard enterprise Company (HP Development Company L.P.), Intel Corporation, IBM Corporation, Microsoft Corporation, SAS Institute, Inc., SAP SE
Recent Strategies Deployed in Machine Learning Market
Partnerships, Collaborations and Agreements:
Jun-2023: Google came into collaboration with Teachmint, a company engaged in offering education-infrastructure solutions. This collaboration aims to improve cloud technologies to enhance the experience for students and teachers. Additionally, through Google Cloud infrastructure, Techmnt aims to promote advanced technologies consisting of data analytics, Artificial Intelligence, and Machine Learning.
Jun-2023: Hewlett Packard Enterprise collaborated with Applied Digital Corporation, a designer, builder, and operator of next-generation digital infrastructure which is developed for High-Performance Computing applications. Through this collaboration, HPE would provide its powerful, energy-efficient supercomputers which are proven to support large-scale AI through Applied Digital's AI cloud service.
Jun-2023: Microsoft signed a partnership with Snowflake, a cloud computing-based data cloud company. Under this partnership, Snowflake would allow joint customers to leverage the new AI models and frameworks increasing the productivity of developers.
Jun-2023: Microsoft partnered with HCLTech, a global technology company. The partnership broadens the adoption of generative AI. This partnership aims to help businesses leverage generative artificial intelligence and develop joint solutions to allow businesses to achieve better outcomes and improve business transformation.
May-2023: Microsoft collaborated with NVIDIA, a US-based global technology company. Following this collaboration, NVIDIA AI Enterprise would be combined with Azure Machine Learning offering a complete Cloud Platform for developers to create, Deploy and Manage AI Applications for large language models.
May-2023: IBM teamed up with SAP SE, a global IT company. Under this collaboration, IBM Watson technology would be combined with SAP solutions to deliver the latest AI-driven automation and insights to help boost innovation and build a more effective and efficient user experience in the SAP solution offering.
May-2023: SAP SE partnered with Google Cloud, a portfolio of cloud computing services delivered by Google. This partnership releases a completely open data offering developed to simplify data landscapes and unlock the power of business data.
Apr-2023: Baidu signed a partnership with Quhuo Limited, a gig economy platform engaged in local life services in China. This partnership marks Quhuo's focus to develop cutting-edge AI technology that would strengthen various business scenarios consisting of front, middle, and back-office functions.
Apr-2023: H2O.ai partnered with Mutt Data, a technology company that helps you develop custom data products using Machine Learning, Data Science, and Big Data to accelerate its business. This partnership would allow companies to strengthen enterprises to accelerate their businesses with data.
Apr-2023: Intel Corporation collaborated with HiddenLayer, an AI application security company. This collaboration aims to provide a complete hardware and software-based ML security solution for enterprises in compliance-focused and regulated industries.
Apr-2023: IBM came into partnership with Moderna, a pharmaceutical and biotechnology company. The partnership aims to support novel technologies, including artificial intelligence and quantum computing to boost messenger RNA research.
Apr-2023: SAS joined hands with Duke Health, a leading academic and health care system. The collaboration aims to design new cloud-based artificial intelligence for healthcare that would focus on enhanced care and provide outcomes, business operations, and health services research.
Mar-2023: AWS came into collaboration with NVIDIA, a US-based software company. The collaboration includes jointly building on-demand AI infrastructure intended for training sophisticated large language models (LLMs) and developing generative AI applications.
Mar-2023: H2O.ai came into partnership with Billigence, a global intelligence consultancy. This partnership aims to boost internal advancement by making it simple to build, deploy and obtain insights from AI-powered predictive models.
Feb-2023: AWS extended its partnership with Hugging Face, a US-based developer of chatbot applications. The partnership focuses on making AI more accessible and includes making AWS Hugging Face's preferred cloud provider, allowing developers to access tools from AWS Trainium, and AWS INferentia, among others.
Sep-2022: Intel came into partnership with Mila, a Montreal-based AI research institute. Under this partnership, More than 20 researchers across Mila and Intel would focus on developing advanced AI techniques to fight global challenges including digital biology, climate change, and new materials discovery.
Aug-2022: SAS came into collaboration with SingleStore, a company engaged in offering databases for operational analytics and cloud-native applications. This collaboration aims to help businesses remove barriers to data access, enhance performance and scalability and uncover critical data-driven insights.
Mar-2022: Google entered into a partnership with BT, a British telecommunications company. Under the partnership, BT utilized a suite of Google Cloud products and services-including cloud infrastructure, machine learning (ML) and artificial intelligence (AI), data analytics, security, and API management-to offer excellent customer experiences, decrease costs, and risks, and create more revenue streams. Google aimed to enable BT to get access to hundreds of new business use cases to solidify its goals around digital offerings and developing hyper-personalized customer engagement.
Product Launches and Product Expansions:
Jul-2023: H2O.ai launched h2oGPT, a portfolio of open-source code repositories for building and utilizing LLMs based on Generative Pretrained Transformers. This launch aims to open an accessible AI ecosystem. The project's primary aim is to build the best truly open-source substitute for closed-source methods.
May-2023: Google released PaLM 2, the next-generation language model. The launched product comes with reasoning, coding, and multilingual capabilities that would enable Google to broaden Bard to the latest languages.
May-2023: Microsoft announced the launch of Microsoft Fabric, the latest analytics and data platform. This launch centers around Microsoft's OneLake data from Google Cloud Platform and Amazon S3. Additionally, the platform combines technologies like Azure Synapse Analytics, Azure Data Factory, and Power BI.
May-2022: Intel launched Habana Gaudi2 AI deep learning processor, a second-generation Habana Gaudi2 AI deep learning processor. The product launched showed around twice the performance on the natural processor and computer vision across Nvidia's A100 80 GB processor.
Acquisitions and Mergers:
Jan-2023: Hewlett Packard took over Pachyderm, a US-based operator of data engineering platform. The blend of HPE and Pachyderm would deliver a combined ML pipeline and platform to advance a customer's journey.
Market Segments covered in the Report:
By Enterprise Size
By Component
By End-use
By Geography
Companies Profiled
Unique Offerings from KBV Research
List of Figures