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市場調查報告書
商品編碼
1292476
自動化機器學習全球市場規模、份額、行業趨勢分析報告:按應用、按產品(解決方案和服務)、按行業、按地區、展望和預測,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,自動化機器學習市場規模預計將達到 91 億美元,預測期內復合年增長率為 42.9%。
根據 KBV Cardinal 矩陣中發布的分析,微軟公司和谷歌有限責任公司(Alphabet Inc.)是市場領導者。 2023 年 5 月,Google Cloud 將擴大與 SAP 的合作夥伴關係,共同構建開放數據和人工智能的未來,推出旨在促進數據格局發展的全面開放數據產品。 該產品允許用戶構建他們的數據。 亞馬遜網路服務公司(Amazon.com, Inc.)、甲骨文公司和惠普企業公司等公司是市場上的主要創新者。
市場增長因素
智能自動化業務轉型的需求不斷增長
隨著我們越來越依賴數據來製定決策並提高運營效率,對智能業務流程的需求也在不斷增長。 此類流程使用機器學習算法來自動化決策並簡化企業運營,從而提高生產力和利潤。 通過利用 AutoML,公司可以提高性能、降低成本、簡化運營並獲得競爭優勢。 此外,人工智能驅動的自動化已被證明可以顯著提高生產力。 通過自動創建和部署機器學習模型,市場可以幫助公司實現這些成果。
更快地做出決策並節省潛在成本
由於機器學習的使用不斷增加,AutoML 市場潛力巨大。 機器學習傳統上是高度專業化的,需要統計、編程和數據分析知識。 隨著 AutoML 技術的引入,組織不再需要數據科學家和機器學習專家來構建和實施 AI 解決方案。 另一方面,AutoML 技術將使公司能夠更容易地進行機器學習,從而產生更廣泛的客戶和用例。 此外,機器學習的民主化將幫助企業擴大服務範圍並開拓新市場,從而提高銷售額和市場份額。
市場抑制因素
機器學習工具的採用較晚
阻礙 AutoML 領域擴展的主要製約因素是這些工具的採用速度緩慢。 儘管 AutoML 有很多好處,包括提高生產力、準確性和可擴展性,但許多公司對於採用它仍猶豫不決。 採用緩慢的主要原因之一是人們對自動化機器學習 (AutoML) 市場及其功能的無知。 許多企業領導者和決策者沒有意識到 AutoML 的好處以及對行業的潛在影響,這可能會阻礙 AutoML 的採用。 因此,由於引入成本低和認知度低而導致採用緩慢預計將阻礙市場擴張。
產品展望
市場細分分為解決方案和服務。 到 2022 年,服務業將在市場中佔據重要的收入份額。 autoML 服務的用戶可以自動化機器學習模型創建和實現中涉及的許多流程,包括特徵工程、超參數調整、模型選擇和部署。 創建這些服務的目的是讓企業和個人更輕鬆地利用機器學習的潛力,而無需對機器學習有深入的了解或專業知識。
解決方案類型 Outlook
根據解決方案類型,市場分為平台和軟件。 2022 年,平台細分市場收入份額最高。 各種技能水平的業務用戶和各種規模的組織都可以快速輕鬆地利用人工智能和機器學習的潛力,通過自動化機器學習平台解決挑戰。 各行業的公司都可以使用這些平台來增強運營、提高客戶保留率,並確定影響從壞賬到處理要求等各個方面的關鍵變量。
應用展望
按應用劃分,市場分為數據處理、特徵工程、模型選擇、超參數優化和調整、模型集成等。 數據處理領域在 2022 年創下了最高的市場收入份額。 數據標準化、清理和轉換只是可以藉助 autoML 實現自動化的數據處理的眾多組件中的一小部分。 數據錯誤檢測和糾正可以使用自動化機器學習 (AutoML) 實現自動化。 這包括識別缺失值、修復數據格式問題以及刪除可能損害機器學習模型準確性的異常值。
行業展望
按行業劃分,可分為 BFSI、零售/電子商務、醫療保健/生命科學、IT/電信、政府/國防、製造、汽車/運輸/物流、媒體/娛樂等。 BFSI 細分市場在 2022 年創造了最大的收入份額,從而引領市場。 BFSI 部門最近加速採用人工智能和機器學習技術,以提高運營效率並增強客戶體驗。 隨著數據受到越來越多的關注,BFSI 應用中對機器學習的需求也在不斷增長。 憑藉大量數據、廉價的計算能力和廉價的存儲,自動化機器學習可以產生準確、快速的結果。
解決方案部署前景
根據解決方案部署,市場分為雲和本地。 2022 年,雲細分市場的收入份額最大。 隨著互聯網連接變得更加可靠和遠程工作變得更加普遍,雲計算變得越來越普遍。 與本地系統相比,基於雲的 AutoML 解決方案更加靈活和可擴展,因為它們可以隨著工作負載和數據量的變化輕鬆擴展和縮減。 此外,基於雲的系統通常提供即用即付定價,這對於具有不同工作負載的公司來說非常經濟。
區域展望
按地區劃分,我們對北美、歐洲、亞太地區和拉美地區 (LAMEA) 的市場進行了分析。 2022 年,北美地區將佔據最高的市場收入份額。 該地區國家是世界上最發達的國家之一。 該地區的汽車機器學習市場正在迅速擴張。 幾家領先的提供商提供從全自動系統到幫助數據科學家創建機器學習模型的解決方案。 該市場的驅動因素是對更快、更有效的方法來開發和部署機器學習模型的需求,以及各行業對人工智能解決方案不斷增長的需求。
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