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市场调查报告书
商品编码
1335905
全球机器学习市场规模、份额、行业趋势分析报告:2023~2030年按公司规模(大型企业、中小企业)、组件(服务、软体、硬体)、最终用途和地区分類的展望和预测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,机器学习市场规模预计将达到 4,084 亿美元,预测期内市场年复合成长率率为 36.7%。
根据KBV Cardinal矩阵发布的分析,Google有限责任公司(Alphabet Inc.)和微软公司是该市场的先驱。 2022 年3 月,Google 与BT 建立合作伙伴关係,以提供卓越的客户体验、降低成本和风险、创造更多收入来源,并让BT 能够存取数百个新业务用例。透过这样做,我们巩固了围绕开发数位的目标产品和超个人化的客户参与。 IBM公司、惠普企业公司和英特尔公司等公司是这个市场的主要创新者。
市场成长要素
透过智慧自动化实现业务转型的需求不断增长
人们越来越依赖资料来推动决策和营运效率,从而推动了对智慧业务流程的需求。这些流程使用机器学习演算法来自动化决策并简化业务运营,从而提高生产力和利润。透过利用 AutoML,企业可以提高效能、降低成本并简化流程以获得竞争优势。此外,事实证明,使用人工智慧的自动化可以显着提高生产力。透过自动化机器学习模型的创建和部署,自动化市场可以帮助公司实现这些成果。
更快做出决策并节省成本
透过采用 AutoML 解决方案,公司可以节省昂贵的基础设施投资和僱用专业人员。此外,人工智慧解决方案的快速开发和部署可以透过提高营运效率和增强决策来节省成本。随着越来越多的公司采用 AutoML 技术,新的使用案例和应用程式将会激增,从而推动创新和市场成长。此外,机器学习的民主化可以帮助公司扩大服务范围并开拓新市场,从而有可能增加销售额和市场份额。
市场抑制因素
法律和道德问题
机器学习需要大量资料,有时包括资料和个人资讯。出于隐私和安全考虑,个人和组织可能会犹豫是否为机器学习目的提供资料。机器学习 (ML) 的使用必须遵守各种法律和法律规范,包括行业特定规则、消费者保护法和反歧视法。不遵守这些标准可能会导致法律责任、经济处罚、公众形象受损和公众信任丧失。由于机器学习实施过程中可能出现的法律问题,组织可能会感到不确定和警惕。预计这些要素将阻碍未来几年的市场扩张。
企业规模展望
根据公司规模,市场分为中小企业和大型企业。到 2022 年,大型企业细分市场将占据市场最大的收入份额。大型企业越来越多地使用云端基础的机器学习平台和服务。可扩展且经济实惠的云端平台架构使训练和部署机器学习模型成为可能。机器学习需要大型企业大量的基础设施支出,因为 Google Cloud AI Platform、Amazon Web Services (AWS) 和 Microsoft Azure Machine Learning 等服务提供预先建置模型、分散式训练功能和基础架构管理。我不需要它。
组件展望
根据组件,市场分为服务、软体和硬体。 2022 年,硬体领域在市场中占据了重要的收入份额。这可能与为机器学习设计的装备的日益普及有关。具有人工智慧和机器学习功能的专用硅处理器的开发有助于硬体的普及。随着 SambaNova Systems 等公司生产更强大的处理设备,该市场预计将继续扩大。
最终用户的展望
依最终用户分类,可分为医疗保健、BFSI、零售、广告/媒体、汽车/运输、农业、製造业等。 2022年,广告和媒体领域以最大的收入份额主导市场。主要趋势之一是超个人化,机器学习演算法会检查大量用户资料并创建个人化的相关广告,从而提高参与度和转换率。目前,人们非常重视利用机器学习来辨识广告诈骗。
区域展望
从区域来看,我们对北美、欧洲、亚太地区和拉丁美洲地区的市场进行了分析。 2022年,北美地区以最大的收入份额引领市场。在北美,机器学习日益增长的社会影响正在引起人们对道德和负责任的人工智慧实践的关注。在开发机器学习模型和演算法时,组织优先考虑公平、课责和开放。减少偏见,保护隐私,并解决人工智慧应用的道德问题。法律体制、规则和标准正在製定中,以监督机器学习在该领域的适当使用。
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