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

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

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.

Scope of the Study

Market Segments covered in the Report:

By Enterprise Size

• Large Enterprises
• SMEs

By Component

• Services
• Software
• Hardware

By End-use

• Advertising & Media
• BFSI
• Automotive & Transportation
• Manufacturing
• Agriculture
• Retail
• Healthcare
• Others

By Geography

• North America
• US
• Canada
• Mexico
• Rest of North America
• Europe
• Germany
• UK
• France
• Russia
• Spain
• Italy
• Rest of Europe
• Asia Pacific
• China
• Japan
• India
• South Korea
• Singapore
• Malaysia
• Rest of Asia Pacific
• LAMEA
• Brazil
• Argentina
• UAE
• Saudi Arabia
• South Africa
• Nigeria
• Rest of LAMEA

Companies Profiled

• 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

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