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市場調査レポート
商品コード
1335905
機械学習の世界市場規模、シェア、産業動向分析レポート:企業規模別(大企業、中小企業)、コンポーネント別(サービス、ソフトウェア、ハードウェア)、最終用途別、地域別の展望と予測、2023年~2030年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 |
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機械学習の世界市場規模、シェア、産業動向分析レポート:企業規模別(大企業、中小企業)、コンポーネント別(サービス、ソフトウェア、ハードウェア)、最終用途別、地域別の展望と予測、2023年~2030年 |
出版日: 2023年07月31日
発行: KBV Research
ページ情報: 英文 280 Pages
納期: 即納可能
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機械学習市場規模は2030年までに4,084億米ドルに達すると予測され、予測期間中のCAGRは36.7%の市場成長率で上昇する見込みです。
KBV Cardinalのマトリックスに掲載された分析によると、Google LLC(Alphabet Inc.)とMicrosoft Corporationが同市場における先駆者です。2022年3月、グーグルはBTとパートナーシップを結び、優れた顧客体験を提供し、コストとリスクを削減し、より多くの収益源を創出し、BTが何百もの新しいビジネス・ユースケースにアクセスできるようにすることで、デジタル・オファリングと超パーソナライズされた顧客エンゲージメントの開発に関する目標を固めました。IBM Corporation、Hewlett-Packard enterprise Company、Intel Corporationなどの企業が、この市場における主要なイノベーターです。
市場成長要因
インテリジェント・オートメーションによるビジネス変革への需要の高まり
意思決定や業務効率を高めるためのデータへの依存度が高まるにつれ、インテリジェントなビジネスプロセスに対するニーズが高まっています。このようなプロセスでは、機械学習アルゴリズムを使用して意思決定を自動化し、企業運営を合理化することで、生産性と利益を向上させます。AutoMLを活用することで、企業はパフォーマンスを向上させ、コストを削減し、プロセスを合理化することができ、競争優位に立つことができます。また、AIを活用した自動化により、生産性が大幅に向上することが実証されています。機械学習モデルの作成と展開を自動化することで、自動化市場は企業がこれらの成果を達成するのを支援することができます。
迅速な意思決定とコスト削減
企業はAutoMLソリューションを採用することで、コストのかかるインフラへの投資や専門人材の雇用にかかる費用を節約することができます。さらに、業務の有効性を高め、意思決定を強化することで、AIソリューションの迅速な開発と導入がコスト削減につながる可能性があります。AutoML技術を採用する企業が増えるにつれ、新たな使用事例やアプリケーションが急増し、イノベーションと市場成長が促進されると思われます。さらに、機械学習の民主化は、企業が提供するサービスを拡大し、新たな市場を開拓するのに役立ち、売上と市場シェアを拡大する可能性があります。
市場抑制要因
法的・倫理的問題
機械学習には、時には機密データや個人情報を含む大量のデータが必要となります。個人や組織は、プライバシーやセキュリティ上の懸念から、ML目的のためにデータを提供することをためらうかもしれないです。機械学習(ML)を利用する際には、業界特有の規則、消費者保護法、差別禁止法など、様々な法律や規制の枠組みを遵守しなければならないです。これらの基準に従わない場合、法的責任、金銭的罰金、イメージダウン、社会的信用の低下などが生じる可能性があります。組織は、ML導入で起こりうる法的問題のために、確信が持てず、警戒心を抱くかもしれないです。これらの要因は、今後数年間の市場拡大を妨げると予想されます。
企業規模の展望
企業規模によって、市場は中小企業と大企業に区分されます。2022年には、大企業セグメントが市場で最大の収益シェアを占める。大企業では、クラウドベースの機械学習プラットフォームとサービスの利用が増加しています。機械学習モデルのトレーニングやデプロイは、クラウドプラットフォームのスケーラブルで手頃なアーキテクチャによって実現可能になっています。Google Cloud AI Platform、Amazon Web Services(AWS)、Microsoft Azure Machine Learningのようなサービスは、事前に構築されたモデル、分散トレーニング機能、インフラ管理を提供するため、機械学習は大企業にとって大きなインフラ支出を必要としないです。
コンポーネントの展望
コンポーネントに基づき、市場はサービス、ソフトウェア、ハードウェアに分けられます。ハードウェア・セグメントは、2022年の市場においてかなりの収益シェアを獲得しました。これは、機械学習用に設計されたギアの人気が高まっていることに関連している可能性があります。AIやMLの機能を備えた特殊なシリコンプロセッサーの開発が、ハードウェアの普及を後押ししています。SambaNova Systemsのような企業によって、より強力な処理装置が生み出されるにつれて、市場は拡大し続けると予測されます。
エンドユーザー別展望
エンドユーザー別では、ヘルスケア、BFSI、小売、広告・メディア、自動車・運輸、農業、製造、その他に分類されます。2022年には、広告・メディア分野が最大の収益シェアで市場を独占しました。主要動向のひとつはハイパー・パーソナライゼーションで、機械学習アルゴリズムが膨大なユーザーデータを調査し、関連性の高い個別の広告を作成することで、エンゲージメントとコンバージョン率を高めています。現在、広告詐欺を特定するために機械学習を採用することにかなりの重点が置かれています。
地域別展望
地域別に見ると、市場は北米、欧州、アジア太平洋、LAMEAで分析されます。2022年には、北米地域が最大の収益シェアで市場をリードしました。北米では、機械学習の社会的影響力の拡大により、道徳的なAIや責任あるAIの実践に注目が集まっています。機械学習モデルやアルゴリズムを開発する際、組織では公正さ、説明責任、公開性が優先されます。バイアスは軽減され、プライバシーは保護され、AIの応用に関する倫理的な問題に取り組んでいます。この分野における機械学習の適切な利用を監督するために、法的枠組み、規則、基準が作られつつあります。
List of Figures
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