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市場調査レポート
商品コード
1292476
自動機械学習の世界市場規模、シェア、産業動向分析レポート:用途別、提供別(ソリューションとサービス)、業界別、地域別展望と予測、2023年~2029年Global 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 |
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自動機械学習の世界市場規模、シェア、産業動向分析レポート:用途別、提供別(ソリューションとサービス)、業界別、地域別展望と予測、2023年~2029年 |
出版日: 2023年05月31日
発行: KBV Research
ページ情報: 英文 361 Pages
納期: 即納可能
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自動機械学習市場規模は2029年までに91億米ドルに達すると予測され、予測期間中のCAGRは42.9%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、Microsoft CorporationとGoogle LLC(Alphabet Inc.)が市場の先行者です。2023年5月、グーグル・クラウドは、オープンデータとAIの未来を共同で構築するためにSAPとの提携を拡大し、データランドスケープを容易にするために開発された本格的なオープンデータ提供を開始しました。この提供により、ユーザーはデータを構築することができます。Amazon Web Services, Inc.(Amazon.com,Inc.)、Oracle Corporation、Hewlett-Packard Enterprise Companyなどの企業は、市場の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、業務を合理化することができ、競争上の優位性を得ることができます。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、同市場は企業がこうした成果を達成するのを支援できます。
迅速な意思決定とコスト削減の可能性
AutoML市場は、機械学習の利用向上により、大きな可能性を秘めています。機械学習はこれまで、統計、プログラミング、データ分析の知識を必要とし、極めて専門的でした。AutoML技術の導入により、組織はAIソリューションの構築と実装にデータサイエンティストや機械学習の専門家のスタッフを必要としなくなっています。一方、AutoMLテクノロジーは、企業が機械学習をより身近に活用することを可能にし、その結果、より幅広い顧客や使用事例が利用できるようになります。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押し上げます。
市場抑制要因
MLツールの導入が遅れている
AutoML分野の拡大を妨げる主な制約は、これらのツールの導入が遅れていることです。AutoMLは生産性、精度、拡張性の向上など多くの利点があるにもかかわらず、多くの企業が導入をためらっています。このように普及が遅れている主な原因の1つは、人々が自動機械学習(AutoML)市場やその機能を知らないことです。AutoMLの採用は、多くの企業リーダーや意思決定者がその利点や業界への潜在的な効果を認識していないという事実によって妨げられている可能性があります。したがって、導入コストの低さや認知度の低さから採用が進まないことが、市場拡大の妨げになると予想されます。
オファリングの展望
市場セグメンテーションは、ソリューションとサービスに区分されます。2022年の市場では、サービス分野が大きな収益シェアを獲得しています。autoMLサービスのユーザーは、フィーチャーエンジニアリング、ハイパーパラメータの調整、モデルの選択、デプロイメントなど、機械学習モデルの作成と実装に関わる多くのプロセスを自動化できます。これらのサービスは、企業や個人が機械学習の可能性を活用する際に、機械学習に関する深い理解や専門知識を必要とせず、より簡単に利用できるように作成されています。
ソリューションタイプの展望
ソリューションの種類では、市場はプラットフォームとソフトウェアに二分されます。プラットフォームセグメントは、2022年の市場で最も高い収益シェアを占めました。あらゆるスキルレベルのビジネスユーザーとあらゆる規模の組織は、自動化された機械学習プラットフォームにより、AIと機械学習の可能性を迅速かつ簡単に利用して課題を解決することができます。あらゆる業界の企業がこれらのプラットフォームを利用することで、業務の強化、顧客維持率の向上、貸し倒れから治療要件まであらゆるものに影響を与える重要な変数の特定が可能になります。
アプリケーションの展望
用途別では、市場はデータ処理、特徴エンジニアリング、モデル選択、ハイパーパラメータ最適化&チューニング、モデルアンサンブル、その他に分けられます。データ処理セグメントは、2022年の市場で最も高い収益シェアを記録しました。データの正規化、クリーニング、変換は、autoMLの助けを借りて自動化できるデータ処理の多くのコンポーネントのほんの一部に過ぎないです。データの誤りの検出と修正は、自動機械学習(AutoML)を使って自動化できます。これには、値の欠落箇所の特定、データ・フォーマットの問題の修正、機械学習モデルの精度を損なう可能性のある異常値の除去などが含まれます。
業界別展望
業界別では、BFSI、小売・eコマース、ヘルスケア・ライフサイエンス、IT・通信、政府・防衛、製造、自動車・運輸・物流、メディア・エンターテインメント、その他に分類されます。BFSIセグメントは、2022年に最大の収益シェアを生み出し、市場をリードしました。BFSI部門は最近、業務の有効性を高め、顧客体験を強化するために、AIとML技術の導入を加速させています。BFSIアプリケーションにおける機械学習の必要性は、データがより注目されるにつれて高まっています。多くのデータ、安価な計算能力、安価なストレージがあれば、自動化された機械学習は正確で迅速な結果を生み出すことができます。
ソリューション展開の展望
ソリューション展開に基づき、市場はクラウドとオンプレミスに二分されます。クラウドセグメントは、2022年の市場で最大の収益シェアを示しました。インターネット接続の信頼性が高まり、リモートワークが一般的になったため、クラウド・コンピューティングがより広く利用されるようになっています。オンプレミスのシステムと比べ、クラウドベースのAutoMLソリューションは、ワークロードやデータ量の変化に合わせて簡単にスケールアップやスケールダウンができるため、より柔軟でスケーラブルです。さらに、クラウドベースのシステムでは従量課金制が頻繁に利用できるため、作業負荷がさまざまな企業にとっては経済的です。
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。北米地域は、2022年の同市場で最も高い収益シェアを獲得しました。同地域の国々は、世界で最も発展した国のひとつに数えられています。同地域では、オートML市場が急速に拡大しています。複数の大手プロバイダーが、完全自動化システムからデータサイエンティストの機械学習モデル作成を支援するものまで、さまざまなソリューションを提供しています。この市場は、機械学習モデルをより迅速かつ効果的に開発・展開する方法へのニーズや、さまざまな業界で人工知能ソリューションへのニーズが高まっていることに後押しされています。
List of Figures
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