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市场调查报告书
商品编码
1292476
自动化机器学习全球市场规模、份额、行业趋势分析报告:按应用、按产品(解决方案和服务)、按行业、按地区、展望和预测,2023-2029Global Automated Machine Learning Market Size, Share & Industry Trends Analysis Report By Application, By Offering (Solution and Services), By Vertical, By Regional Outlook and Forecast, 2023 - 2029 |
到 2029 年,自动化机器学习市场规模预计将达到 91 亿美元,预测期内復合年增长率为 42.9%。
根据 KBV Cardinal 矩阵中发布的分析,微软公司和谷歌有限责任公司(Alphabet Inc.)是市场领导者。 2023 年 5 月,Google Cloud 将扩大与 SAP 的合作伙伴关係,共同构建开放数据和人工智能的未来,推出旨在促进数据格局发展的全面开放数据产品。 该产品允许用户构建他们的数据。 亚马逊网路服务公司(Amazon.com, Inc.)、甲骨文公司和惠普企业公司等公司是市场上的主要创新者。
市场增长因素
智能自动化业务转型的需求不断增长
随着我们越来越依赖数据来製定决策并提高运营效率,对智能业务流程的需求也在不断增长。 此类流程使用机器学习算法来自动化决策并简化企业运营,从而提高生产力和利润。 通过利用 AutoML,公司可以提高性能、降低成本、简化运营并获得竞争优势。 此外,人工智能驱动的自动化已被证明可以显着提高生产力。 通过自动创建和部署机器学习模型,市场可以帮助公司实现这些成果。
更快地做出决策并节省潜在成本
由于机器学习的使用不断增加,AutoML 市场潜力巨大。 机器学习传统上是高度专业化的,需要统计、编程和数据分析知识。 随着 AutoML 技术的引入,组织不再需要数据科学家和机器学习专家来构建和实施 AI 解决方案。 另一方面,AutoML 技术将使公司能够更容易地进行机器学习,从而产生更广泛的客户和用例。 此外,机器学习的民主化将帮助企业扩大服务范围并开拓新市场,从而提高销售额和市场份额。
市场抑制因素
机器学习工具的采用较晚
阻碍 AutoML 领域扩展的主要製约因素是这些工具的采用速度缓慢。 儘管 AutoML 有很多好处,包括提高生产力、准确性和可扩展性,但许多公司对于采用它仍犹豫不决。 采用缓慢的主要原因之一是人们对自动化机器学习 (AutoML) 市场及其功能的无知。 许多企业领导者和决策者没有意识到 AutoML 的好处以及对行业的潜在影响,这可能会阻碍 AutoML 的采用。 因此,由于引入成本低和认知度低而导致采用缓慢预计将阻碍市场扩张。
产品展望
市场细分分为解决方案和服务。 到 2022 年,服务业将在市场中占据重要的收入份额。 autoML 服务的用户可以自动化机器学习模型创建和实现中涉及的许多流程,包括特征工程、超参数调整、模型选择和部署。 创建这些服务的目的是让企业和个人更轻鬆地利用机器学习的潜力,而无需对机器学习有深入的了解或专业知识。
解决方案类型 Outlook
根据解决方案类型,市场分为平台和软件。 2022 年,平台细分市场收入份额最高。 各种技能水平的业务用户和各种规模的组织都可以快速轻鬆地利用人工智能和机器学习的潜力,通过自动化机器学习平台解决挑战。 各行业的公司都可以使用这些平台来增强运营、提高客户保留率,并确定影响从坏账到处理要求等各个方面的关键变量。
应用展望
按应用划分,市场分为数据处理、特征工程、模型选择、超参数优化和调整、模型集成等。 数据处理领域在 2022 年创下了最高的市场收入份额。 数据标准化、清理和转换只是可以藉助 autoML 实现自动化的数据处理的众多组件中的一小部分。 数据错误检测和纠正可以使用自动化机器学习 (AutoML) 实现自动化。 这包括识别缺失值、修復数据格式问题以及删除可能损害机器学习模型准确性的异常值。
行业展望
按行业划分,可分为 BFSI、零售/电子商务、医疗保健/生命科学、IT/电信、政府/国防、製造、汽车/运输/物流、媒体/娱乐等。 BFSI 细分市场在 2022 年创造了最大的收入份额,从而引领市场。 BFSI 部门最近加速采用人工智能和机器学习技术,以提高运营效率并增强客户体验。 随着数据受到越来越多的关注,BFSI 应用中对机器学习的需求也在不断增长。 凭藉大量数据、廉价的计算能力和廉价的存储,自动化机器学习可以产生准确、快速的结果。
解决方案部署前景
根据解决方案部署,市场分为云和本地。 2022 年,云细分市场的收入份额最大。 随着互联网连接变得更加可靠和远程工作变得更加普遍,云计算变得越来越普遍。 与本地系统相比,基于云的 AutoML 解决方案更加灵活和可扩展,因为它们可以随着工作负载和数据量的变化轻鬆扩展和缩减。 此外,基于云的系统通常提供即用即付定价,这对于具有不同工作负载的公司来说非常经济。
区域展望
按地区划分,我们对北美、欧洲、亚太地区和拉美地区 (LAMEA) 的市场进行了分析。 2022 年,北美地区将占据最高的市场收入份额。 该地区国家是世界上最发达的国家之一。 该地区的汽车机器学习市场正在迅速扩张。 几家领先的提供商提供从全自动系统到帮助数据科学家创建机器学习模型的解决方案。 该市场的驱动因素是对更快、更有效的方法来开发和部署机器学习模型的需求,以及各行业对人工智能解决方案不断增长的需求。
The Global Automated Machine Learning Market size is expected to reach $9.1 billion by 2029, rising at a market growth of 42.9% CAGR during the forecast period.
Model selection is one of the major applications of automated machine learning. AutoML tools can expedite the prototyping and iteration phase of machine learning projects. By quickly exploring different models and configurations, data scientists can iterate and refine their models more efficiently. This agility enables faster experimentation and iteration cycles, ultimately accelerating the development of high-quality machine learning solutions. Thereby, Model Selection acquired $111 million revenue in 2022.
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. or instance, In August, 2022, Alibaba Cloud entered into a collaboration agreement with the Hong Kong University of Science and Technology (HKUST) for supporting the research work of the HKUST researchers, etc. The partnership reflects Alibaba Cloud's commitment to nurturing technology talent and supporting local innovation ecosystems. Additionally, In March, 2023, AWS came into collaboration with NVIDIA for training sophisticated large language models (LLMs) and developing generative AI applications.
Based on the Analysis presented in the KBV Cardinal matrix; Microsoft Corporation, and Google LLC (Alphabet Inc.) are the forerunners in the Market. In May, 2023, Google Cloud extended its partnership with SAP for jointly building the future of open data and AI, and bringing in a full-fledged open data offering developed to make data landscapes easier. This offering allows users to build data could. Companies such as Amazon Web Services, Inc. (Amazon.com, Inc.), Oracle Corporation, and Hewlett-Packard Enterprise Company are some of the key innovators in 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 operations, giving them a competitive advantage. In addition, AI-powered automation has been demonstrated to significantly increase productivity. By automating the creation and deployment of machine learning models, the market can assist firms in achieving these types of outcomes.
Using potential for quicker decision-making and cost reduction
The AutoML market has enormous potential due to the improved use of machine learning. Machine learning has always required substantial statistics, programming, and data analysis knowledge and has been extremely specialized. Organizations no longer require a staff of data scientists and machine learning specialists to construct and implement AI solutions due to the introduction of AutoML technologies. AutoML technologies, on the other hand, allow businesses to make more accessible use of machine learning, thereby rendering it more available to a wider range of customers and use cases. Furthermore, the democratization of machine learning can help companies expand their offers and tap into new markets, boosting sales and market share.
Market Restraining Factors
The adoption of ML tools is slow
A primary restriction impeding the expansion of the AutoML sector is the delayed uptake of these tools. Many businesses are hesitant to implement AutoML despite its many advantages, such as improved productivity, accuracy, and scalability. One of the main causes of this sluggish acceptance is that people are unaware of the automated machine learning (AutoML) market or its capabilities. The adoption of AutoML may be hampered by the fact that many corporate leaders and decision-makers may not be aware of its advantages and the potential effects on their industry. Therefore, it is anticipated that the lack of adoption because of the low implementation cost and the low awareness will impede market expansion.
Offering Outlook
Based on offering, the market is segmented into solutions and services. The services segment acquired a substantial revenue share in the market in 2022. Users of autoML services can automate a number of processes involved in creating and implementing machine learning models, including feature engineering, tweaking hyperparameters, model selection, and deployment. These services are created to make it simpler for companies and individuals to utilize the potential of machine learning without needing a deep understanding of or expertise in the subject.
Solution Type Outlook
Under the solutions type, the market is bifurcated into platform and software. The platform segment held the highest revenue share in the market in 2022. Business users of all skill levels and organizations of all sizes may quickly and simply use the potential of AI and machine learning to solve challenges due to automated machine learning platforms. Companies from all industries can use these platforms to enhance operations, boost client retention, and pinpoint crucial variables that affect everything from loan default to medical treatment requirements.
Application Outlook
On the basis of application, the market is divided into data processing, feature engineering, model selection, hyperparameter optimization & tuning, model ensembling and others. The data processing segment registered the highest revenue share in the market in 2022. Data normalization, cleaning, and transformation are just a few of the many components of data processing that may be automated with the help of autoML. Data mistake detection and correction can be automated using automated machine learning (AutoML). This includes figuring out where values are missing, fixing data formatting issues, and eliminating outliers that can compromise the precision of machine learning models.
Vertical Outlook
By vertical, the market is classified into BFSI, retail & ecommerce, healthcare & life sciences, IT & telecom, government & defense, manufacturing, automotive, transportations, & logistics, media & entertainment and others. The BFSI segment led the market by generating the maximum revenue share in 2022. The BFSI sector has recently implemented AI and ML technologies at a faster rate to boost operational effectiveness and enhance the customer experience. The need for machine learning in BFSI applications increases as data receives more attention. With a lot of data, inexpensive computing power, and cheap storage, automated machine learning can generate accurate and quick results.
Solution Deployment Outlook
Based on the solution deployment, the market is bifurcated into cloud and on-premise. The cloud segment witnessed the largest revenue share in the market in 2022. Since internet connections have become more dependable and remote work has become more common, cloud computing has become more widely used. In comparison to on-premises systems, cloud-based AutoML solutions are more flexible and scalable since they are simple to scale up or down to match changes in workload or data volume. Additionally, pay-as-you-go pricing is frequently available with cloud-based systems, which can be more economical for businesses with varying workloads.
Regional Outlook
Region-wise, the market is analyzed across North America, Europe, Asia Pacific, and LAMEA. The North America region generated the highest revenue share in the market in 2022. The nations in the region rank among the most developed in the world. In the region, the autoML market is expanding quickly. Several major providers are providing a variety of solutions, from fully automated systems to those that help data scientists create machine learning models. The market is being pushed by the need for quicker and more effective ways to develop and deploy machine learning models, as well as a growing need for artificial intelligence solutions across various industries.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Oracle Corporation, IBM Corporation, Microsoft Corporation, Google LLC (Alphabet Inc.), Amazon Web Services, Inc. (Amazon.com, Inc.), Salesforce, Inc., Hewlett-Packard enterprise Company, Teradata Corporation, Alibaba Cloud (Alibaba Group Holding Limited) and Databricks, Inc.
Recent Strategies Deployed in Automated Machine Learning Market
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.
Jul-2022: IBM took over Databand.ai, a leading provider of data observability software. This acquisition aimed to provide IBM with the most comprehensive set of observability offerings for IT across applications, data, and machine learning and would continue to provide IBM's customers and partners with the technology they require to provide trustworthy data and AI at scale.
Jun-2021: Hewlett Packard Enterprise completed the acquisition of Determined AI, a San Francisco-based startup. This acquisition aimed to provide a strong and robust software stack to train AI models quicker, at any scale, utilizing its open-source machine learning (ML) platform.
Partnerships, Collaborations and Agreements:
May-2023: Google Cloud extended its partnership with SAP, a Germany-based software company. The partnership focuses on jointly building the future of open data and AI and bringing in a full-fledged open data offering developed to make data landscapes easier. This offering allows users to build data could.
Apr-2023: Oracle extended its partnership with GitLab, a US-based technology company. The collaboration enables users to run AI and ML workloads along with GPU-enabled GitLab runners on the OCI, Oracle Cloud Infrastructure. Further, GitLab's vision for accuracy and speed perfectly aligns with Oracle's goals.
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.
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.
Nov-2022: Microsoft signed an agreement with Lockheed Martin, a US-based company operating in the aerospace and defense industry. The agreement focuses on four key areas for the Department of Defense. The key areas include Artificial Intelligence/Machine Learning (AI/ML), Classified Cloud Innovations, 5G.MIL Programs, Digital Transformation, and Modeling and Simulation Capabilities.
Oct-2022: Oracle extended its partnership with Nvidia, a US-based manufacturer, and designer of discrete graphics processing units. The partnership involves supporting customers in the faster adoption of AI services. This partnership would lead to delivering both the companies' respective expertise to support clients across various markets.
Sep-2022: Salesforce extended its partnership with Amazon Web Services (AWS), a US-based provider of cloud-based web platforms. The partnership would enable users to develop personalized AI models through Amazon SageMaker.
Aug-2022: Alibaba Cloud entered into a collaboration agreement with the Hong Kong University of Science and Technology (HKUST), a public university in Hong Kong. The collaboration involves teaming up on technology research, supporting the research work of the HKUST researchers, etc. The partnership reflects Alibaba Cloud's commitment to nurturing technology talent and supporting local innovation ecosystems.
Aug-2022: Oracle Cloud Infrastructure came into collaboration with Anaconda, a US-based developer of data science platform. The collaboration focuses on providing secure open-source R and Python tools by incorporating the data science platform's repository across OCI's ML and AI services offerings. Through this collaboration, the companies aim at introducing open-source innovation to the enterprises and support in applying Ai and ML to the users' critical and important business and research initiatives.
Jun-2021: AWS signed a partnership agreement with Salesforce, a US-based provider of enterprise cloud computing solutions. This partnership would enable users to use Salesforce and AWS' capabilities together to rapidly develop and deploy business applications that would advance digital transformation.
Product Launches and Expansions:
May-2023: Oracle launched OML4Py 2.0. The new ML product features, new data types, and makes available their in-database algorithms, Extreme Gradient Boosting, Exponential Smoothing, and Non-negative Matrix Factorization.
Mar-2023: Databricks launched Databricks Model Serving, a real-time machine learning intended for the Lakehouse, Databricks' platform. The Model Serving makes the model building and maintenance process easier. The new offering would enable the customers to deploy models and enjoy lower time to production, lowered cost of ownership, and decreased burden.
May-2021: Google Cloud unveiled Vertex AI, a machine learning platform. Vertex AI is intended for developers, making it easier for them to maintain, and deploy AI models. The newly launched product aims at reducing the time to ROI for the users.
Feb-2021: Salesforce launched Intelligent Document Automation (IDA) technology intended for the healthcare industry. The new technology supports the users in digitizing their document management processes and is powered by Amazon Textract.
Market Segments covered in the Report:
By Application
By Offering
By Vertical
By Geography
Companies Profiled
Unique Offerings from KBV Research
List of Figures