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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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    Report

  • 361 Pages
  • May 2023
  • Region: Global
  • Marqual IT Solutions Pvt. Ltd (KBV Research)
  • ID: 5833532
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.

The Cardinal Matrix - Market Competition Analysis

Based on the Analysis presented in the The 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.



The leading players in the market are competing with diverse innovative offerings to remain competitive in the market. The above illustration shows the percentage of revenue shared by some of the leading companies in the market. The leading players of the market are adopting various strategies in order to cater demand coming from the different industries. The key developmental strategies in the market are Partnerships & Collaborations.

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.

Strategies Deployed in the Market

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 Product 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.

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.

Scope of the Study

By Application

  • Data Processing
  • Feature Engineering
  • Model Selection
  • Hyperparameter Optimization & Tuning
  • Model Ensembling
  • Others

By Offering

  • Solution
    • Type
      • Platform
      • Software
    • Deployment
      • Cloud
      • On-premise
    • Services

By Vertical

  • BFSI
  • IT & Telecom
  • Retail & Ecommerce
  • Media & Entertainment
  • Healthcare & Life Sciences
  • Government & Defense
  • Manufacturing
  • Automotive, Transportations, & Logistics
  • 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

Key Market Players

List of Companies Profiled in the Report:

  • 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)
  • Databricks, Inc.

Unique Offerings

  • Exhaustive coverage
  • The highest number of Market tables and figures
  • Subscription-based model available
  • Guaranteed best price
  • Assured post sales research support with 10% customization free

Table of Contents

Chapter 1. Market Scope & Methodology
1.1 Market Definition
1.2 Objectives
1.3 Market Scope
1.4 Segmentation
1.4.1 Global Automated Machine Learning Market, by Application
1.4.2 Global Automated Machine Learning Market, by Offering
1.4.3 Global Automated Machine Learning Market, by Vertical
1.4.4 Global Automated Machine Learning Market, by Geography
1.5 Research Methodology
Chapter 2. Market Overview
2.1 Introduction
2.1.1 Overview
2.1.1.1 Market composition & scenario
2.2 Key Factors Impacting the Market
2.2.1 Market Drivers
2.2.2 Market Restraints
Chapter 3. Competition Analysis - Global
3.1 The Cardinal Matrix
3.2 Recent Industry Wide Strategic Developments
3.2.1 Partnerships, Collaborations and Agreements
3.2.2 Product Launches and Product Expansions
3.2.3 Acquisition and Mergers
3.3 Market Share Analysis, 2021
3.4 Top Winning Strategies
3.4.1 Key Leading Strategies: Percentage Distribution (2019-2023)
3.4.2 Key Strategic Move: (Partnerships, Collaborations & Agreements: 2021, Jun - 2023, May) Leading Players
Chapter 4. Global Automated Machine Learning Market by Application
4.1 Global Data Processing Market by Region
4.2 Global Feature Engineering Market by Region
4.3 Global Model Selection Market by Region
4.4 Global Hyperparameter Optimization & Tuning Market by Region
4.5 Global Model Ensembling Market by Region
4.6 Global Others Market by Region
Chapter 5. Global Automated Machine Learning Market by Offering
5.1 Global Solution Market by Region
5.2 Global Automated Machine Learning Market by Solution Type
5.2.1 Global Platform Market by Region
5.2.2 Global Software Market by Region
5.3 Global Automated Machine Learning Market by Solution Deployment
5.3.1 Global Cloud Market by Region
5.3.2 Global On-premise Market by Region
5.4 Global Services Market by Region
Chapter 6. Global Automated Machine Learning Market by Vertical
6.1 Global BFSI Market by Region
6.2 Global IT & Telecom Market by Region
6.3 Global Retail & Ecommerce Market by Region
6.4 Global Media & Entertainment Market by Region
6.5 Global Healthcare & Life Sciences Market by Region
6.6 Global Government & Defense Market by Region
6.7 Global Manufacturing Market by Region
6.8 Global Automotive, Transportations, & Logistics Market by Region
6.9 Global Others Market by Region
Chapter 7. Global Automated Machine Learning Market by Region
7.1 North America Automated Machine Learning Market
7.1.1 North America Automated Machine Learning Market by Application
7.1.1.1 North America Data Processing Market by Country
7.1.1.2 North America Feature Engineering Market by Country
7.1.1.3 North America Model Selection Market by Country
7.1.1.4 North America Hyperparameter Optimization & Tuning Market by Country
7.1.1.5 North America Model Ensembling Market by Country
7.1.1.6 North America Others Market by Country
7.1.2 North America Automated Machine Learning Market by Offering
7.1.2.1 North America Solution Market by Country
7.1.2.2 North America Automated Machine Learning Market by Solution Type
7.1.2.2.1 North America Platform Market by Country
7.1.2.2.2 North America Software Market by Country
7.1.2.3 North America Automated Machine Learning Market by Solution Deployment
7.1.2.3.1 North America Cloud Market by Country
7.1.2.3.2 North America On-premise Market by Country
7.1.2.4 North America Services Market by Country
7.1.3 North America Automated Machine Learning Market by Vertical
7.1.3.1 North America BFSI Market by Country
7.1.3.2 North America IT & Telecom Market by Country
7.1.3.3 North America Retail & Ecommerce Market by Country
7.1.3.4 North America Media & Entertainment Market by Country
7.1.3.5 North America Healthcare & Life Sciences Market by Country
7.1.3.6 North America Government & Defense Market by Country
7.1.3.7 North America Manufacturing Market by Country
7.1.3.8 North America Automotive, Transportations, & Logistics Market by Country
7.1.3.9 North America Others Market by Country
7.1.4 North America Automated Machine Learning Market by Country
7.1.4.1 US Automated Machine Learning Market
7.1.4.1.1 US Automated Machine Learning Market by Application
7.1.4.1.2 US Automated Machine Learning Market by Offering
7.1.4.1.3 US Automated Machine Learning Market by Vertical
7.1.4.2 Canada Automated Machine Learning Market
7.1.4.2.1 Canada Automated Machine Learning Market by Application
7.1.4.2.2 Canada Automated Machine Learning Market by Offering
7.1.4.2.3 Canada Automated Machine Learning Market by Vertical
7.1.4.3 Mexico Automated Machine Learning Market
7.1.4.3.1 Mexico Automated Machine Learning Market by Application
7.1.4.3.2 Mexico Automated Machine Learning Market by Offering
7.1.4.3.3 Mexico Automated Machine Learning Market by Vertical
7.1.4.4 Rest of North America Automated Machine Learning Market
7.1.4.4.1 Rest of North America Automated Machine Learning Market by Application
7.1.4.4.2 Rest of North America Automated Machine Learning Market by Offering
7.1.4.4.3 Rest of North America Automated Machine Learning Market by Vertical
7.2 Europe Automated Machine Learning Market
7.2.1 Europe Automated Machine Learning Market by Application
7.2.1.1 Europe Data Processing Market by Country
7.2.1.2 Europe Feature Engineering Market by Country
7.2.1.3 Europe Model Selection Market by Country
7.2.1.4 Europe Hyperparameter Optimization & Tuning Market by Country
7.2.1.5 Europe Model Ensembling Market by Country
7.2.1.6 Europe Others Market by Country
7.2.2 Europe Automated Machine Learning Market by Offering
7.2.2.1 Europe Solution Market by Country
7.2.2.2 Europe Automated Machine Learning Market by Solution Type
7.2.2.2.1 Europe Platform Market by Country
7.2.2.2.2 Europe Software Market by Country
7.2.2.3 Europe Automated Machine Learning Market by Solution Deployment
7.2.2.3.1 Europe Cloud Market by Country
7.2.2.3.2 Europe On-premise Market by Country
7.2.2.4 Europe Services Market by Country
7.2.3 Europe Automated Machine Learning Market by Vertical
7.2.3.1 Europe BFSI Market by Country
7.2.3.2 Europe IT & Telecom Market by Country
7.2.3.3 Europe Retail & Ecommerce Market by Country
7.2.3.4 Europe Media & Entertainment Market by Country
7.2.3.5 Europe Healthcare & Life Sciences Market by Country
7.2.3.6 Europe Government & Defense Market by Country
7.2.3.7 Europe Manufacturing Market by Country
7.2.3.8 Europe Automotive, Transportations, & Logistics Market by Country
7.2.3.9 Europe Others Market by Country
7.2.4 Europe Automated Machine Learning Market by Country
7.2.4.1 Germany Automated Machine Learning Market
7.2.4.1.1 Germany Automated Machine Learning Market by Application
7.2.4.1.2 Germany Automated Machine Learning Market by Offering
7.2.4.1.3 Germany Automated Machine Learning Market by Vertical
7.2.4.2 UK Automated Machine Learning Market
7.2.4.2.1 UK Automated Machine Learning Market by Application
7.2.4.2.2 UK Automated Machine Learning Market by Offering
7.2.4.2.3 UK Automated Machine Learning Market by Vertical
7.2.4.3 France Automated Machine Learning Market
7.2.4.3.1 France Automated Machine Learning Market by Application
7.2.4.3.2 France Automated Machine Learning Market by Offering
7.2.4.3.3 France Automated Machine Learning Market by Vertical
7.2.4.4 Russia Automated Machine Learning Market
7.2.4.4.1 Russia Automated Machine Learning Market by Application
7.2.4.4.2 Russia Automated Machine Learning Market by Offering
7.2.4.4.3 Russia Automated Machine Learning Market by Vertical
7.2.4.5 Spain Automated Machine Learning Market
7.2.4.5.1 Spain Automated Machine Learning Market by Application
7.2.4.5.2 Spain Automated Machine Learning Market by Offering
7.2.4.5.3 Spain Automated Machine Learning Market by Vertical
7.2.4.6 Italy Automated Machine Learning Market
7.2.4.6.1 Italy Automated Machine Learning Market by Application
7.2.4.6.2 Italy Automated Machine Learning Market by Offering
7.2.4.6.3 Italy Automated Machine Learning Market by Vertical
7.2.4.7 Rest of Europe Automated Machine Learning Market
7.2.4.7.1 Rest of Europe Automated Machine Learning Market by Application
7.2.4.7.2 Rest of Europe Automated Machine Learning Market by Offering
7.2.4.7.3 Rest of Europe Automated Machine Learning Market by Vertical
7.3 Asia Pacific Automated Machine Learning Market
7.3.1 Asia Pacific Automated Machine Learning Market by Application
7.3.1.1 Asia Pacific Data Processing Market by Country
7.3.1.2 Asia Pacific Feature Engineering Market by Country
7.3.1.3 Asia Pacific Model Selection Market by Country
7.3.1.4 Asia Pacific Hyperparameter Optimization & Tuning Market by Country
7.3.1.5 Asia Pacific Model Ensembling Market by Country
7.3.1.6 Asia Pacific Others Market by Country
7.3.2 Asia Pacific Automated Machine Learning Market by Offering
7.3.2.1 Asia Pacific Solution Market by Country
7.3.2.2 Asia Pacific Automated Machine Learning Market by Solution Type
7.3.2.2.1 Asia Pacific Platform Market by Country
7.3.2.2.2 Asia Pacific Software Market by Country
7.3.2.3 Asia Pacific Automated Machine Learning Market by Solution Deployment
7.3.2.3.1 Asia Pacific Cloud Market by Country
7.3.2.3.2 Asia Pacific On-premise Market by Country
7.3.2.4 Asia Pacific Services Market by Country
7.3.3 Asia Pacific Automated Machine Learning Market by Vertical
7.3.3.1 Asia Pacific BFSI Market by Country
7.3.3.2 Asia Pacific IT & Telecom Market by Country
7.3.3.3 Asia Pacific Retail & Ecommerce Market by Country
7.3.3.4 Asia Pacific Media & Entertainment Market by Country
7.3.3.5 Asia Pacific Healthcare & Life Sciences Market by Country
7.3.3.6 Asia Pacific Government & Defense Market by Country
7.3.3.7 Asia Pacific Manufacturing Market by Country
7.3.3.8 Asia Pacific Automotive, Transportations, & Logistics Market by Country
7.3.3.9 Asia Pacific Others Market by Country
7.3.4 Asia Pacific Automated Machine Learning Market by Country
7.3.4.1 China Automated Machine Learning Market
7.3.4.1.1 China Automated Machine Learning Market by Application
7.3.4.1.2 China Automated Machine Learning Market by Offering
7.3.4.1.3 China Automated Machine Learning Market by Vertical
7.3.4.2 Japan Automated Machine Learning Market
7.3.4.2.1 Japan Automated Machine Learning Market by Application
7.3.4.2.2 Japan Automated Machine Learning Market by Offering
7.3.4.2.3 Japan Automated Machine Learning Market by Vertical
7.3.4.3 India Automated Machine Learning Market
7.3.4.3.1 India Automated Machine Learning Market by Application
7.3.4.3.2 India Automated Machine Learning Market by Offering
7.3.4.3.3 India Automated Machine Learning Market by Vertical
7.3.4.4 South Korea Automated Machine Learning Market
7.3.4.4.1 South Korea Automated Machine Learning Market by Application
7.3.4.4.2 South Korea Automated Machine Learning Market by Offering
7.3.4.4.3 South Korea Automated Machine Learning Market by Vertical
7.3.4.5 Singapore Automated Machine Learning Market
7.3.4.5.1 Singapore Automated Machine Learning Market by Application
7.3.4.5.2 Singapore Automated Machine Learning Market by Offering
7.3.4.5.3 Singapore Automated Machine Learning Market by Vertical
7.3.4.6 Malaysia Automated Machine Learning Market
7.3.4.6.1 Malaysia Automated Machine Learning Market by Application
7.3.4.6.2 Malaysia Automated Machine Learning Market by Offering
7.3.4.6.3 Malaysia Automated Machine Learning Market by Vertical
7.3.4.7 Rest of Asia Pacific Automated Machine Learning Market
7.3.4.7.1 Rest of Asia Pacific Automated Machine Learning Market by Application
7.3.4.7.2 Rest of Asia Pacific Automated Machine Learning Market by Offering
7.3.4.7.3 Rest of Asia Pacific Automated Machine Learning Market by Vertical
7.4 LAMEA Automated Machine Learning Market
7.4.1 LAMEA Automated Machine Learning Market by Application
7.4.1.1 LAMEA Data Processing Market by Country
7.4.1.2 LAMEA Feature Engineering Market by Country
7.4.1.3 LAMEA Model Selection Market by Country
7.4.1.4 LAMEA Hyperparameter Optimization & Tuning Market by Country
7.4.1.5 LAMEA Model Ensembling Market by Country
7.4.1.6 LAMEA Others Market by Country
7.4.2 LAMEA Automated Machine Learning Market by Offering
7.4.2.1 LAMEA Solution Market by Country
7.4.2.2 LAMEA Automated Machine Learning Market by Solution Type
7.4.2.2.1 LAMEA Platform Market by Country
7.4.2.2.2 LAMEA Software Market by Country
7.4.2.3 LAMEA Automated Machine Learning Market by Solution Deployment
7.4.2.3.1 LAMEA Cloud Market by Country
7.4.2.3.2 LAMEA On-premise Market by Country
7.4.2.4 LAMEA Services Market by Country
7.4.3 LAMEA Automated Machine Learning Market by Vertical
7.4.3.1 LAMEA BFSI Market by Country
7.4.3.2 LAMEA IT & Telecom Market by Country
7.4.3.3 LAMEA Retail & Ecommerce Market by Country
7.4.3.4 LAMEA Media & Entertainment Market by Country
7.4.3.5 LAMEA Healthcare & Life Sciences Market by Country
7.4.3.6 LAMEA Government & Defense Market by Country
7.4.3.7 LAMEA Manufacturing Market by Country
7.4.3.8 LAMEA Automotive, Transportations, & Logistics Market by Country
7.4.3.9 LAMEA Others Market by Country
7.4.4 LAMEA Automated Machine Learning Market by Country
7.4.4.1 Brazil Automated Machine Learning Market
7.4.4.1.1 Brazil Automated Machine Learning Market by Application
7.4.4.1.2 Brazil Automated Machine Learning Market by Offering
7.4.4.1.3 Brazil Automated Machine Learning Market by Vertical
7.4.4.2 Argentina Automated Machine Learning Market
7.4.4.2.1 Argentina Automated Machine Learning Market by Application
7.4.4.2.2 Argentina Automated Machine Learning Market by Offering
7.4.4.2.3 Argentina Automated Machine Learning Market by Vertical
7.4.4.3 UAE Automated Machine Learning Market
7.4.4.3.1 UAE Automated Machine Learning Market by Application
7.4.4.3.2 UAE Automated Machine Learning Market by Offering
7.4.4.3.3 UAE Automated Machine Learning Market by Vertical
7.4.4.4 Saudi Arabia Automated Machine Learning Market
7.4.4.4.1 Saudi Arabia Automated Machine Learning Market by Application
7.4.4.4.2 Saudi Arabia Automated Machine Learning Market by Offering
7.4.4.5 Saudi Arabia Automated Machine Learning Market by Solution Deployment
7.4.4.5.1 Saudi Arabia Automated Machine Learning Market by Vertical
7.4.4.6 South Africa Automated Machine Learning Market
7.4.4.6.1 South Africa Automated Machine Learning Market by Application
7.4.4.6.2 South Africa Automated Machine Learning Market by Offering
7.4.4.6.3 South Africa Automated Machine Learning Market by Vertical
7.4.4.7 Nigeria Automated Machine Learning Market
7.4.4.7.1 Nigeria Automated Machine Learning Market by Application
7.4.4.7.2 Nigeria Automated Machine Learning Market by Offering
7.4.4.7.3 Nigeria Automated Machine Learning Market by Vertical
7.4.4.8 Rest of LAMEA Automated Machine Learning Market
7.4.4.8.1 Rest of LAMEA Automated Machine Learning Market by Application
7.4.4.8.2 Rest of LAMEA Automated Machine Learning Market by Offering
7.4.4.8.3 Rest of LAMEA Automated Machine Learning Market by Vertical
Chapter 8. Company Profiles
8.1 Oracle Corporation
8.1.1 Company Overview
8.1.2 Financial Analysis
8.1.3 Segmental and Regional Analysis
8.1.4 Research & Development Expense
8.1.5 Recent Strategies and Developments
8.1.5.1 Partnerships, Collaborations, and Agreements
8.1.5.2 Product Launches and Product Expansions
8.1.6 SWOT Analysis
8.2 IBM Corporation
8.2.1 Company Overview
8.2.2 Financial Analysis
8.2.3 Regional & Segmental Analysis
8.2.4 Research & Development Expenses
8.2.5 Recent Strategies and Developments
8.2.5.1 Acquisition and Mergers
8.2.6 SWOT Analysis
8.3 Microsoft Corporation
8.3.1 Company Overview
8.3.2 Financial Analysis
8.3.3 Segmental and Regional Analysis
8.3.4 Research & Development Expenses
8.3.5 Recent Strategies and Developments
8.3.5.1 Partnerships, Collaborations, and Agreements
8.3.6 SWOT Analysis
8.4 Google LLC (Alphabet Inc.)
8.4.1 Company Overview
8.4.2 Financial Analysis
8.4.3 Segmental and Regional Analysis
8.4.4 Research & Development Expense
8.4.5 Recent Strategies and Developments
8.4.5.1 Partnerships, Collaborations, and Agreements
8.4.5.2 Product Launches and Product Expansions
8.4.6 SWOT Analysis
8.5 Amazon Web Services, Inc. (Amazon.com, Inc.)
8.5.1 Company Overview
8.5.2 Financial Analysis
8.5.3 Segmental Analysis
8.5.4 Recent Strategies and Developments
8.5.4.1 Partnerships, Collaborations, and Agreements
8.5.5 SWOT Analysis
8.6 Salesforce, Inc.
8.6.1 Company Overview
8.6.2 Financial Analysis
8.6.3 Regional Analysis
8.6.4 Research & Development Expense
8.6.5 Recent Strategies and Developments
8.6.5.1 Partnerships, Collaborations, and Agreements
8.6.5.2 Product Launches and Product Expansions
8.6.6 SWOT Analysis
8.7 Hewlett Packard Enterprise Company
8.7.1 Company Overview
8.7.2 Financial Analysis
8.7.3 Segmental and Regional Analysis
8.7.4 Research & Development Expense
8.7.5 Recent Strategies and Developments
8.7.5.1 Acquisition and Mergers
8.7.6 SWOT Analysis
8.8 Teradata Corporation
8.8.1 Company Overview
8.8.2 Financial Analysis
8.8.3 Regional Analysis
8.8.4 Research & Development Expense
8.8.5 SWOT Analysis
8.9 Alibaba Cloud (Alibaba Group Holding Limited)
8.9.1 Company Overview
8.9.2 Financial Analysis
8.9.3 Segmental Analysis
8.9.4 Recent Strategies and Developments
8.9.4.1 Partnerships, Collaborations, and Agreements
8.10. Databricks, Inc.
8.10.1 Company Overview
8.10.2 Recent Strategies and Developments
8.10.2.1 Product Launches and Product Expansions

Companies Mentioned

  • 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)
  • Databricks, Inc.

Methodology

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