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Automated Machine Learning (AutoML) Market by Offering (Solutions & Services), Application (Data Processing, Model Selection, Hyperparameter Optimization & Tuning, Feature Engineering, Model Ensembling), Vertical and Region - Global Forecast to 2028

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    Report

  • 349 Pages
  • May 2023
  • Region: Global
  • Markets and Markets
  • ID: 5797662
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Growing Data Volume and Complexity is to Drive the Market

The market for Automated Machine Learning is projected to grow from USD 1.0 billion in 2023 to USD 6.4 billion by 2028, at a CAGR of 44.6% during the forecast period. Explainable AI is a crucial aspect of AutoML that aims to provide transparency into how machine learning models make predictions. By using explainable AI techniques, such as feature importance and decision trees, businesses can gain insights into how their models work and make more informed decisions. 

The BFSI vertical is projected to be the largest market during the forecast period.

AutoML is an emerging technology used in the BFSI sectors to automate iterative and time-consuming tasks, build machine learning models with productivity, efficiency, and high scale, and minimize the knowledge-based resources needed to implement and train machine learning models. AutoML can be used for credit card fraud detection, risk assessment, and real-time gain and loss prediction for investments. AutoML can also help reduce deployment time by automating data extraction and algorithms, eliminating manual parts of the analyses, and significantly reducing deployment time. For instance, the Consensus Corporation reduced its deployment time from 3-4 weeks to eight hours using AutoML. AutoML can help enterprises boost insights and enhance model accuracy by minimizing the chances of error or bias in the BFSI sector. AutoML provides several benefits to the BFSI industry. It helps to reduce the need for manual data science processes, which can be complex and time-consuming and can accelerate the work of data scientists. AutoML can also help optimize business performance driven by data, enabling business leaders to make decisions with real-time analytics.

Among Applications, the model ensembling segment is registered to grow at the highest CAGR during the forecast period.

AutoML for model ensembling involves the use of automated techniques to create a collection of models that can be combined to improve prediction accuracy. Ensembling is a popular technique in machine learning that involves combining the predictions of multiple models to generate a more accurate final prediction. AutoML can use various techniques for model ensembling, such as bagging, boosting, and stacking. AutoML can automatically create multiple models using different algorithms and hyperparameters and combine them using ensembling techniques. This can improve the robustness and accuracy of the final model, as it reduces the risk of overfitting and leverages the strengths of different algorithms. The benefit of using AutoML for model ensembling is that it can automate the process of selecting and combining models, which can save time and effort for data scientists. AutoML can also evaluate the performance of different ensembling methods and select the one that performs the best on the given dataset.

Among services, the consulting services segment is anticipated to account for the largest market size during the forecast period.

Consulting services are typically offered by third-party vendors or consulting firms, providing expertise and guidance on machine learning strategy and implementation. Consulting services can help organizations evaluate their data readiness, identify use cases, and develop a roadmap for implementing machine learning within their organization. AutoML consulting services can help organizations navigate the complex landscape of machine learning tools and platforms and make informed decisions about which tools and technologies to use based on their specific needs and goals. Consultants can also guide data preparation, model selection, and hyperparameter tuning and can help organizations evaluate the performance and effectiveness of their machine-learning models. Consultants may work onsite or remotely and provide ongoing support and guidance throughout the machine learning lifecycle. By providing expertise, guidance, and education, consultants can help organizations make informed decisions and achieve better results with their machine-learning initiatives.

North America to account for the largest market size during the forecast period.

North America is estimated to account for the largest share of the Automated Machine Learning market. The global market for Automated Machine Learning is dominated by North America. North America is the highest revenue-generating region in the global Automated Machine Learning market, with the US constituting the highest market share, followed by Canada. The region has a high adoption rate of machine learning and artificial intelligence technologies across various industries, including healthcare, finance, and retail, which is expected to drive the demand for AutoML solutions. Moreover, the presence of a large number of data-driven startups and companies in the region is further fueling the growth of the AutoML market in North America.

Breakdown of primaries

In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the Automated Machine Learning market.

  • By Company: Tier I: 35%, Tier II: 45%, and Tier III: 20%
  • By Designation: C-Level Executives: 35%, Directors: 25%, and Others: 40%
  • By Region: APAC: 30%, Europe: 20%, North America: 40%, MEA: 5%, Latin America: 5%

Major vendors offering Automted Machine Learning solutions and services across the globe are IBM (US), Oracle (US), Microsoft (US), ServiceNow (US), Google (US), Baidu (China), AWS (US), Alteryx (US), Salesforce (US), Altair (US), Teradata (US), H2O.ai (US), DataRobot (US), BigML (US), Databricks (US), Dataiku (France), Alibaba Cloud (China), Appier (Taiwan), Squark (US), Aible (US), Datafold (US), Boost.ai (Norway), Tazi.ai (US), Akkio (US), Valohai (Finland), dotData (US), Qlik (US), Mathworks (US), HPE (US), and SparkCognition (US).

Research Coverage

The market study covers Automated Machine Learning across segments. It aims at estimating the market size and the growth potential across different segments, such as offering, application, vertical, and region. It includes an in-depth competitive analysis of the key players in the market, along with their company profiles, key observations related to product and business offerings, recent developments, and key market strategies.

Key Benefits of Buying the Report 

The report would provide the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall market for Automated Machine Learning and its subsegments. It would help stakeholders understand the competitive landscape and gain more insights better to position their business and plan suitable go-to-market strategies. It also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.

The report provides insights on the following pointers: 

  • Analysis of key drivers (Growing demand for improved customer satisfaction and personalized product recommendations through AutoML, Increasing need for accurate fraud detection, Growing data volume and complexity, Rising need to transform businesses with Intelligent automation using AutoML), restraints (Machine learning tools are being slowly adopted, Lack of standardization and regulations), opportunities (Capitalizing on the growing demand for AI-enabled solutions, Integration with complementary technologies, Seizing opportunities for faster decision-making and cost savings), and challenges (Increasing shortage of skilled talent, Difficulty in Interpreting and explaining AutoML models, Data privacy in AutoML) influencing the growth of the Automated Machine Learning market 
  • Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the Automated Machine Learning market.
  • Market Development: Comprehensive information about lucrative markets - the report analyses the Automated Machine Learning market across varied regions 
  • Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in Automated Machine Learning market strategies; the report also helps stakeholders understand the pulse of the Automated Machine Learning market and provides them with information on key market drivers, restraints, challenges, and opportunities
  • Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading players such as IBM (US), Google (US), AWS(US), Microsoft (US), and Salesforce (US), among others in the Automated Machine Learning market.

Table of Contents

1 Introduction
1.1 Study Objectives
1.2 Market Definition
1.2.1 Inclusions and Exclusions
1.3 Market Scope
1.3.1 Market Segmentation
1.3.2 Regions Covered
1.4 Years Considered
1.5 Currency Considered
Table 1 USD Exchange Rates, 2020-2022
1.6 Stakeholders

2 Research Methodology
2.1 Research Data
Figure 1 Automated Machine Learning Market: Research Design
2.1.1 Secondary Data
2.1.1.1 Key Data from Secondary Sources
2.1.2 Primary Data
2.1.2.1 Key Data from Primary Sources
2.1.2.2 Key Primary Interview Participants
2.1.2.3 Breakup of Primary Profiles
2.1.2.4 Key Industry Insights
2.2 Data Triangulation
2.3 Market Size Estimation
Figure 2 Market: Top-Down and Bottom-Up Approaches
2.3.1 Top-Down Approach
2.3.2 Bottom-Up Approach
Figure 3 Approach 1 (Supply Side): Revenue from Offerings of Automated Machine Learning (Automl) Market Players
Figure 4 Approach 2 - Bottom-Up (Supply Side): Collective Revenue from Offerings of Market Players
Figure 5 Approach 3 - Bottom-Up (Supply Side): Revenue and Subsequent Market Estimation from Market Offerings
Figure 6 Approach 4 - Bottom-Up (Demand Side): Share of Market Offerings Through Overall Automated Machine Learning Spending
2.4 Market Forecast
Table 2 Factor Analysis
2.5 Research Assumptions
2.6 Limitations and Risk Assessment
2.7 Impact of Recession on the Global Market
Table 3 Impact of Recession on the Global Market

3 Executive Summary
Table 4 Global Automated Machine Learning Market Size and Growth Rate, 2017-2022 (USD Million, Y-O-Y%)
Table 5 Global Market Size and Growth Rate, 2023-2028 (USD Million, Y-O-Y%)
Figure 7 Solutions Segment to Lead Market in 2023
Figure 8 Platforms Segment to Account for Largest Share in 2023
Figure 9 Om-Premises Segment to Account for Larger Share During Forecast Period
Figure 10 Consulting Services Segment to Account for Largest Share in 2023
Figure 11 Data Processing Segment to Account for Largest Share in 2023
Figure 12 Bfsi Segment to Lead Market in 2023
Figure 13 North America to Account for Largest Share in 2023

4 Premium Insights
4.1 Attractive Market Opportunities for Players in Automated Machine Learning Market
Figure 14 Rising Demand for Platforms to Transfer Data from On-Premises to Cloud to Drive Learning Market
4.2 Market, by Vertical
Figure 15 Retail & E-commerce Segment to Account for Largest Share During Forecast Period
4.3 Market, by Region
Figure 16 North America to Account for Largest Share by 2028
4.4 Market, by Offering and Key Vertical
Figure 17 Solutions and Bfsi Segments to Account for Significant Share by 2028

5 Market Overview and Industry Trends
5.1 Introduction
5.2 Market Dynamics
Figure 18 Automated Machine Learning Market: Drivers, Restraints, Opportunities, and Challenges
5.2.1 Drivers
5.2.1.1 Growing Demand for Improved Customer Satisfaction and Personalized Product Recommendations Through Automl
5.2.1.2 Increasing Need for Accurate Fraud Detection
5.2.1.3 Growing Data Volume and Complexity
5.2.1.4 Rising to Need to Transform Businesses with Intelligent Automation Using Automl
5.2.2 Restraints
5.2.2.1 Slow Adoption of Machine Learning Tools
5.2.2.2 Lack of Standardization and Regulations
5.2.3 Opportunities
5.2.3.1 Growing Demand for Ai-Enabled Solutions Across Industries
5.2.3.2 Seamless Integration Between Technologies
5.2.3.3 Increased Accessibility of Machine Learning Solutions
5.2.4 Challenges
5.2.4.1 Growing Shortage of Skilled Workforce
5.2.4.2 Difficulty in Interpreting and Explaining Automl Models
5.2.4.3 Rising Threat to Data Privacy
5.3 Case Study Analysis
5.3.1 Real Estate
5.3.1.1 Case Study 1: Ascendas Singbridge Group Improved Real Estate Decision-Making by Leveraging Datarobot's Automl Platform
5.3.1.2 Case Study 2: G5 Employed H2O.Ai's Driverless Ai Platform to Address Challenges in Identifying Productive Leads
5.3.2 Bfsi
5.3.2.1 Case Study 1: Robotica Helped Avant Automate Key Processes and Streamline Lending Operations
5.3.2.2 Case Study 2: Domestic and General Partnered with Datarobot to Improve Customer Service Capabilities
5.3.2.3 Case Study 3: H2O.Ai's Machine Learning Platform Enabled Paypal to Strengthen Fraud Detection Capabilities
5.3.3 Retail & Ecommerce
5.3.3.1 Case Study 1: California Design Den Partnered with Google Cloud Platform to Implement Machine Learning Solutions
5.3.4 It/Ites
5.3.4.1 Case Study 1: Contentree Helped Consensus Simplify Data Wrangling Process and Make It Efficient
5.3.4.2 Case Study 2: Datarobot's Automated Machine Learning Platform Helped Demyst Automate Data Science Processes
5.3.5 Healthcare & Lifesciences
5.3.5.1 Case Study 1: Datarobot Helped Evariant Automate Patient Risk Stratification and Readmission Prediction
5.3.6 Media & Entertainment
5.3.6.1 Case Study 1: Meredith Corporation Worked with Google Cloud to Build Data Analytics Platform to Handle Large Volumes of Data
5.3.7 Transportation & Logistics
5.3.7.1 Case Study 1: Dmway Enabled Pgl to Integrate and Analyze Data from Multiple Sources
5.3.8 Energy & Utilities
5.3.8.1 Case Study 1: Sparkcognition Helped Oil & Gas Industry to Build Predictive Models by Leveraging Automated Machine Learning Solutions
5.4 Ecosystem Analysis
Figure 19 Ecosystem Analysis
Table 6 Automated Machine Learning (Automl) Market: Platform Providers
Table 7 Market: Service Providers
Table 8 Market: Technology Providers
Table 9Market: Regulatory Bodies
5.5 History of Automated Machine Learning
5.6 Automated Machine Learning Pipeline Framework
Figure 20 Automated Machine Learning Pipeline Framework
Table 10 Automated Machine Learning Pipeline Framework
5.7 Value Chain Analysis
Figure 21 Value Chain Analysis
5.7.1 Data Collection & Preparation
5.7.2 Algorithm Development
5.7.3 Model Training
5.7.4 Model Testing and Validation
5.7.5 Deployment and Integration
5.7.6 Maintenance and Support
5.8 Pricing Model Analysis
Table 11 Automated Machine Learning Market: Pricing Levels
5.9 Patent Analysis
5.9.1 Methodology
5.9.2 Document Type
Table 12 Patents Filed, 2018-2021
5.9.3 Innovation and Patent Applications
Figure 22 Total Number of Patents Granted, 2021-2023
5.9.3.1 Top Applicants
Figure 23 Top Ten Companies with Highest Number of Patent Applications, 2018-2021
Table 13 Top 20 Patent Owners, 2018-2021
Table 14 List of Patents in Automated Machine Learning Market, 2021-2023
5.10 Automated Machine Learning Techniques
5.10.1 Bayesian Optimization
5.10.2 Reinforcement Learning
5.10.3 Evolutionary Algorithm
5.10.4 Gradient Approaches
5.11 Comparison of Autoai and Automl Solutions
Table 15 Comparison Between Autoai and Automl Solutions
5.12 Business Models of Automl
5.12.1 Api Models
5.12.2 As-A-Service Model
5.12.3 Cloud Model
5.13 Technology Analysis
5.13.1 Related Technologies
5.13.1.1 Supervised Learning
5.13.1.2 Unsupervised Learning
5.13.1.3 Natural Language Processing
5.13.1.4 Computer Vision
5.13.1.5 Transfer Learning
5.13.2 Allied Technologies
5.13.2.1 Cloud Computing
5.13.2.2 Robotics
5.13.2.3 Federated Learning
5.14 Porter's Five Forces Analysis
Figure 24 Porter's Five Forces Analysis
Table 16 Porter's Five Forces Analysis
5.14.1 Threat from New Entrants
5.14.2 Threat from Substitutes
5.14.3 Bargaining Power of Suppliers
5.14.4 Bargaining Power of Buyers
5.14.5 Intensity of Competitive Rivalry
5.15 Key Conferences & Events
Table 17 Detailed List of Conferences & Events, 2023-2024
5.16 Regulatory Landscape
5.16.1 Regulatory Bodies, Government Agencies, and Other Organizations
Table 18 North America: Regulatory Bodies, Government Agencies, and Other Organizations
Table 19 Europe: Regulatory Bodies, Government Agencies, and Other Organizations
Table 20 Asia-Pacific: List of Regulatory Bodies, Government Agencies, and Other Organizations
Table 21 RoW: Regulatory Bodies, Government Agencies, and Other Organizations
5.16.1.1 North America
5.16.1.1.1 US
5.16.1.1.2 Canada
5.16.1.2 Europe
5.16.1.3 Asia-Pacific
5.16.1.3.1 South Korea
5.16.1.3.2 China
5.16.1.3.3 India
5.16.1.4 Middle East & Africa
5.16.1.4.1 UAE
5.16.1.4.2 Ksa
5.16.1.4.3 Bahrain
5.16.1.5 Latin America
5.16.1.5.1 Brazil
5.16.1.5.2 Mexico
5.17 Key Stakeholders & Buying Criteria
5.17.1 Key Stakeholders in Buying Process
Figure 25 Influence of Stakeholders on Buying Process for Top Three Verticals
Table 22 Influence of Stakeholders on Buying Process for Top Three Verticals
5.17.2 Buying Criteria
Figure 26 Key Buying Criteria for Top Three Verticals
Table 23 Key Buying Criteria for Top Three Verticals
5.18 Best Practices in Automated Machine Learning Market
5.19 Disruptions Impacting Buyers/Clients in the Market
Figure 27 Market: Disruptions Impacting Buyers/Clients
5.20 Future Directions of Automated Machine Learning Landscape
Table 24 Short-Term Roadmap, 2023-2025
Table 25 Mid-Term Roadmap, 2026-2028
Table 26 Long-Term Roadmap, 2029-2030

6 Automated Machine Learning Market, by Offering
6.1 Introduction
6.1.1 Offerings: Market Drivers
Figure 28 Services Segment to Grow at Higher CAGR During Forecast Period
Table 27 Market, by Offering, 2017-2022 (USD Million)
Table 28 Market, by Offering, 2023-2028 (USD Million)
6.2 Solutions
Table 29 Solutions: Automated Machine Learning Market, by Region, 2017-2022 (USD Million)
Table 30 Solutions: Market, by Region, 2023-2028 (USD Million)
6.2.1 Automated Machine Learning Solutions, by Type
Figure 29 Platforms Segment to Witness Higher Growth During Forecast Period
Table 31 Solutions: Market, by Type, 2017-2022 (USD Million)
Table 32 Solutions: Market, by Type, 2023-2028 (USD Million)
6.2.1.1 Platforms
6.2.1.1.1 Ease of Use and Deployment to Drive Adoption of Automated Machine Learning Platforms
Table 33 Platforms: Market, by Region, 2017-2022 (USD Million)
Table 34 Platforms: Market, by Region, 2023-2028 (USD Million)
6.2.1.2 Software
6.2.1.2.1 Ease of Integration into Existing Machine Learning Workflows to Boost Deployment of Automated Machine Learning Software Solutions
Table 35 Software: Market, by Region, 2017-2022 (USD Million)
Table 36 Software: Market, by Region, 2023-2028 (USD Million)
6.2.2 Automated Machine Learning Solutions, by Deployment
Figure 30 On-Premises Segment to Witness Higher CAGR During Forecast Period
Table 37 Solutions: Market, by Deployment, 2017-2022 (USD Million)
Table 38 Solutions: Market, by Deployment, 2023-2028 (USD Million)
6.2.2.1 On-Premises
6.2.2.1.1 Increased Control Over Data and Infrastructure to Drive On-Premises Deployment of Automated Machine Learning Solutions
Table 39 On-Premises: Market, by Region, 2017-2022 (USD Million)
Table 40 On-Premises: Market, by Region, 2023-2028 (USD Million)
6.2.2.2 Cloud
6.2.2.2.1 Flexibility and Scalability of Cloud-Based Automl Solutions to Boost Market Growth
Table 41 Cloud: Market, by Region, 2017-2022 (USD Million)
Table 42 Cloud: Market, by Region, 2023-2028 (USD Million)
6.3 Services
Figure 31 Training, Support, and Maintenance Segment to Account for Largest Share During Forecast Period
Table 43 Services: Automated Machine Learning Market, by Type, 2017-2022 (USD Million)
Table 44 Services: Market, by Type, 2023-2028 (USD Million)
Table 45 Services: Market, by Region, 2017-2022 (USD Million)
Table 46 Services: Market, by Region, 2023-2028 (USD Million)
6.3.1 Consulting Services
6.3.1.1 Rising Demand for Expert Guidance on Machine Learning Strategies to Drive Growth of Automated Machine Learning Consulting Services
Table 47 Consulting Services: Market, by Region, 2017-2022 (USD Million)
Table 48 Consulting Services: Market, by Region, 2023-2028 (USD Million)
6.3.2 Deployment and Integration
6.3.2.1 Rising Demand for Integrating Machine Learning Models into Existing Workflows and Applications to Boost Adoption of Automl Deployment and Integration Services
Table 49 Deployment and Integration: Market, by Region, 2017-2022 (USD Million)
Table 50 Deployment and Integration: Market, by Region, 2023-2028 (USD Million)
6.3.3 Training, Support, and Maintenance
6.3.3.1 Rising Preference for Optimal Model Performance and Accuracy to Drive Use of Automl Training, Support, and Maintenance Services
Table 51 Training, Support, and Maintenance: Market, by Region, 2017-2022 (USD Million)
Table 52 Training, Support, and Maintenance: Market, by Region, 2023-2028 (USD Million)

7 Automated Machine Learning Market, by Application
7.1 Introduction
7.1.1 Applications: Market Drivers
Figure 32 Data Processing Segment to Lead Market During Forecast Period
Table 53 Market, by Application, 2017-2022 (USD Million)
Table 54 Market, by Application, 2023-2028 (USD Million)
7.2 Data Processing
7.2.1 Growing Need to Detect and Correct Data Errors to Drive Adoption of Automl Solutions for Data Processing
Table 55 Data Processing: Market, by Region, 2017-2022 (USD Million)
Table 56 Data Processing: Market, by Region, 2023-2028 (USD Million)
7.2.2 Cleaning
7.2.3 Transformation
7.2.4 Visualization
7.3 Model Selection
7.3.1 Rising Demand for Automated Techniques to Handle Complex Data to Boost Growth of Automl Solutions for Model Selection
Table 57 Model Selection: Market, by Region, 2017-2022 (USD Million)
Table 58 Model Selection: Market, by Region, 2023-2028 (USD Million)
7.3.2 Scaling
7.3.3 Monitoring
7.3.4 Versioning
7.4 Hyperparameter Optimization & Tuning
7.4.1 Increased Adoption of Automl Algorithms for Hyperparameter Optimization to Drive Market Growth
Table 59 Hyperparameter Tuning & Optimization: Automated Machine Learning Market, by Region, 2017-2022 (USD Million)
Table 60 Hyperparameter Tuning & Optimization: Market, by Region, 2023-2028 (USD Million)
7.4.2 Grid Search
7.4.3 Random Search
7.4.4 Bayesian Search
7.5 Feature Engineering
7.5.1 Rising Need to Transform Raw Data into a Set of Features for Use in Machine Learning Models to Boost Adoption of Automl Solutions in Feature Engineering
Table 61 Feature Engineering: Market, by Region, 2017-2022 (USD Million)
Table 62 Feature Engineering: Market, by Region, 2023-2028 (USD Million)
7.6 Model Ensembling
7.6.1 Growing Importance of Improving Prediction Accuracy to Propel Growth of Automl Solutions for Model Ensembling
7.6.2 Infrastructure & Format
7.6.3 Integration
7.6.4 Maintenance
7.7 Other Applications
Table 65 Other Applications: Market, by Region, 2017-2022 (USD Million)
Table 66 Other Applications: Market, by Region, 2023-2028 (USD Million)

8 Automated Machine Learning Market, by Vertical
8.1 Introduction
8.1.1 Verticals: Market Drivers
Figure 33 Bfsi Segment to Account for Larger Market Size During Forecast Period
Table 67 Market, by Vertical, 2017-2022 (USD Million)
Table 68 Market, by Vertical, 2023-2028 (USD Million)
8.2 Banking, Financial Services, and Insurance
8.2.1 Need to Optimize Business Performance with Real-Time Analytics to Drive Use of Automl Solutions in the Bfsi Sector
Table 69 Bfsi: Use Cases
Table 70 Bfsi: Market, by Region, 2017-2022 (USD Million)
Table 71 Bfsi: Market, by Region, 2023-2028 (USD Million)
Table 72 Bfsi: Market, by Sub-Vertical, 2017-2022 (USD Million)
Table 73 Bfsi: Market, by Sub-Vertical, 2023-2028 (USD Million)
8.2.2 Credit Scoring
8.2.3 Fraud Detection
8.2.4 Risk Analysis & Management
8.2.5 Other Bfsi Sub-Verticals
8.3 Healthcare & Life Sciences
8.3.1 Demand for Improved Diagnoses and Personalized Treatment Plans to Drive Market for Ai and Ml Solutions for Healthcare & Life Sciences Industry
Table 74 Healthcare & Lifesciences: Use Cases
Table 75 Healthcare & Life Sciences: Automated Machine Learning Market, by Region, 2017-2022 (USD Million)
Table 76 Healthcare & Life Sciences: Market, by Region, 2023-2028 (USD Million)
Table 77 Healthcare & Life Sciences: Market, by Sub-Vertical, 2017-2022 (USD Million)
Table 78 Healthcare & Life Sciences: Market, by Sub-Vertical, 2023-2028 (USD Million)
8.3.2 Anomaly Detection
8.3.3 Disease Diagnosis
8.3.4 Drug Discovery
8.3.5 Other Healthcare Sub-Verticals
8.4 Retail & E-commerce
8.4.1 Growing Need for Personalization and Optimization in Highly Competitive Industries to Boost Market Growth
Table 79 Retail & E-commerce: Use Cases
Table 80 Retail & E-commerce: Automated Machine Learning Market, by Region, 2017-2022 (USD Million)
Table 81 Retail & E-commerce: Market, by Region, 2023-2028 (USD Million)
Table 82 Retail & E-commerce: Market, by Sub-Vertical, 2017-2022 (USD Million)
Table 83 Retail & E-commerce: Market, by Sub-Vertical, 2023-2028 (USD Million)
8.4.2 Demand Forecasting
8.4.3 Price Optimization
8.4.4 Recommendation Engines
8.4.5 Sentiment Analysis
8.4.6 Social Media Analytics
8.4.7 Chatbots for Customer Service & Support
8.4.8 Other Retail & Ecommerce Sub-Verticals
8.5 Manufacturing
8.5.1 Automl Solutions to Optimize Manufacturing Process and Improve Efficiency
Table 84 Manufacturing: Use Cases
Table 85 Manufacturing: Automated Machine Learning Market, by Region, 2017-2022 (USD Million)
Table 86 Manufacturing: Market, by Region, 2023-2028 (USD Million)
Table 87 Manufacturing: Market, by Sub-Vertical, 2017-2022 (USD Million)
Table 88 Manufacturing: Market, by Sub-Vertical, 2023-2028 (USD Million)
8.5.2 Predictive Maintenance
8.5.3 Quality Control
8.5.4 Robotic Process Automation
8.5.5 Supply Chain Optimization
8.5.6 Other Manufacturing Sub-Verticals
8.6 Government & Defense
8.6.1 Rising Need to Empower National Security and Public Services to Drive Adoption of Automl Platforms in Government & Defense Sector
Table 89 Government & Defense: Use Cases
Table 90 Government & Defense: Automated Machine Learning Market, by Region, 2017-2022 (USD Million)
Table 91 Government & Defense: Market, by Region, 2023-2028 (USD Million)
Table 92 Government & Defense: Market, by Sub-Vertical, 2017-2022 (USD Million)
Table 93 Government & Defense: Market, by Sub-Vertical, 2023-2028 (USD Million)
8.6.2 Cybersecurity Threat Detection
8.6.3 Fraud Detection & Prevention
8.6.4 Natural Disaster Management
8.6.5 Customer Service Chatbots
8.6.6 Other Government & Defense Sub-Verticals
8.7 Telecommunications
8.7.1 Need for Enhanced Customer Service to Boost Use of Automl Solutions in the Telecommunications Industry
Table 94 Telecommunications: Use Cases
Table 95 Telecommunications: Automated Machine Learning Market, by Region, 2017-2022 (USD Million)
Table 96 Telecommunications: Market, by Region, 2023-2028 (USD Million)
Table 97 Telecommunications: Market, by Sub-Vertical, 2017-2022 (USD Million)
Table 98 Telecommunications: Market, by Sub-Vertical, 2023-2028 (USD Million)
8.7.2 Cybersecurity Threat Detection
8.7.3 Network Optimization
8.7.4 Predictive Maintenance
8.7.5 Fraud Detection & Prevention
8.7.6 Chatbots & Virtual Assistance
8.7.7 Other Telecommunications Sub-Verticals
8.8 It/Ites
8.8.1 Need to Optimize Processes and Enhance Cybersecurity to Propel Growth of Market for It/Ites Sector
Table 99 It/Ites: Use Cases
Table 100 It/Ites: Automated Machine Learning Market, by Region, 2017-2022 (USD Million)
Table 101 It/Ites: Market, by Region, 2023-2028 (USD Million)
Table 102 It/Ites: Market, by Sub-Vertical, 2017-2022 (USD Million)
Table 103 It/Ites: Market, by Sub-Vertical, 2023-2028 (USD Million)
8.8.2 Predictive Maintenance
8.8.3 Virtual Assistants for Customer Support
8.8.4 Network Optimization
8.8.5 Other It/Ites Sub-Verticals
8.9 Automotive, Transportation, and Logistics
8.9.1 Automated Machine Learning Solutions to Enable Organizations to Leverage Data and Gain Insights for Better Business Decisions
Table 104 Automotive, Transportation, and Logistics: Use Cases
Table 105 Automotive, Transportation, and Logistics: Automated Machine Learning Market, by Region, 2017-2022 (USD Million)
Table 106 Automotive, Transportation, and Logistics: Market, by Region, 2023-2028 (USD Million)
Table 107 Automotive, Transportation, and Logistics: Market, by Sub-Vertical, 2017-2022 (USD Million)
Table 108 Automotive, Transportation, and Logistics: Market, by Sub-Vertical, 2023-2028 (USD Million)
8.9.2 Autonomous Vehicles
8.9.3 Route Optimization
8.9.4 Fuel Efficiency Prediction & Optimization
8.9.5 Human Machine Interface (HMI)
8.9.6 Semi-Autonomous Driving
8.9.7 Robotic Process Automation
8.9.8 Other Automotive, Transportation, and Logistics Sub-Verticals
8.10 Media & Entertainment
8.10.1 Use of Automl Solutions to Ensure Improved Content Discovery
Table 109 Media & Entertainment: Use Cases
Table 110 Media & Entertainment: Automated Machine Learning Market, by Region, 2017-2022 (USD Million)
Table 111 Media & Entertainment: Market, by Region, 2023-2028 (USD Million)
Table 112 Media & Entertainment: Market, by Sub-Vertical, 2017-2022 (USD Million)
Table 113 Media & Entertainment: Market, by Sub-Vertical, 2023-2028 (USD Million)
8.10.2 Image & Speech Recognition
8.10.3 Recommendation Systems
8.10.4 Sentiment Analysis
8.10.5 Other Media & Entertainment Sub-Verticals
8.11 Other Verticals
Table 114 Other Verticals: Market, by Region, 2017-2022 (USD Million)
Table 115 Other Verticals: Market, by Region, 2023-2028 (USD Million)

9 Automated Machine Learning Market, by Region
9.1 Introduction
Figure 34 Asia-Pacific to Grow at Highest CAGR During Forecast Period
Figure 35 India to Grow at Highest CAGR During Forecast Period
Table 116 Market, by Region, 2017-2022 (USD Million)
Table 117 Market, by Region, 2023-2028 (USD Million)
9.2 North America
9.2.1 North America: Market Drivers
9.2.2 North America: Recession Impact
Figure 36 North America: Automated Machine Learning Market Snapshot
Table 118 North America: Market, by Offering, 2017-2022 (USD Million)
Table 119 North America: Market, by Offering, 2023-2028 (USD Million)
Table 120 North America: Market, by Type, 2017-2022 (USD Million)
Table 121 North America: Market, by Type, 2023-2028 (USD Million)
Table 122 North America: Market, by Deployment, 2017-2022 (USD Million)
Table 123 North America: Market, by Deployment, 2023-2028 (USD Million)
Table 124 North America: Market, by Service, 2017-2022 (USD Million)
Table 125 North America: Market, by Service, 2023-2028 (USD Million)
Table 126 North America: Market, by Application, 2017-2022 (USD Million)
Table 127 North America: Market, by Application, 2023-2028 (USD Million)
Table 128 North America: Market, by Vertical, 2017-2022 (USD Million)
Table 129 North America: Market, by Vertical, 2023-2028 (USD Million)
Table 130 North America: Market, by Country, 2017-2022 (USD Million)
Table 131 North America: Market, by Country, 2023-2028 (USD Million)
9.2.3 US
9.2.3.1 Growing Demand for Efficient Ways to Build and Deploy Machine Learning Models to Drive Market Growth
Table 132 US: Automated Machine Learning Market, by Offering, 2017-2022 (USD Million)
Table 133 US: Market, by Offering, 2023-2028 (USD Million)
Table 134 US: Market, by Type, 2017-2022 (USD Million)
Table 135 US: Market, by Type, 2023-2028 (USD Million)
Table 136 US: Market, by Deployment, 2017-2022 (USD Million)
Table 137 US: Market, by Deployment, 2023-2028 (USD Million)
Table 138 US: Market, by Service, 2017-2022 (USD Million)
Table 139 US: Market, by Service, 2023-2028 (USD Million)
9.2.4 Canada
9.2.4.1 Rising Adoption of Machine Learning Applications in Various Industries Across Canada to Fuel Market Growth
9.3 Europe
9.3.1 Europe: Market Drivers
9.3.2 Europe: Recession Impact
Table 140 Europe: Automated Machine Learning Market, by Offering, 2017-2022 (USD Million)
Table 141 Europe: Market, by Offering, 2023-2028 (USD Million)
Table 142 Europe: Market, by Type, 2017-2022 (USD Million)
Table 143 Europe: Market, by Type, 2023-2028 (USD Million)
Table 144 Europe: Market, by Deployment, 2017-2022 (USD Million)
Table 145 Europe: Market, by Deployment, 2023-2028 (USD Million)
Table 146 Europe: Market, by Service, 2017-2022 (USD Million)
Table 147 Europe: Market, by Service, 2023-2028 (USD Million)
Table 148 Europe: Market, by Application, 2017-2022 (USD Million)
Table 149 Europe: Market, by Application, 2023-2028 (USD Million)
Table 150 Europe: Market, by Vertical, 2017-2022 (USD Million)
Table 151 Europe: Market, by Vertical, 2023-2028 (USD Million)
Table 152 Europe: Market, by Country, 2017-2022 (USD Million)
Table 153 Europe: Market, by Country, 2023-2028 (USD Million)
Table 154 UK: Automated Machine Learning Market, by Offering, 2017-2022 (USD Million)
Table 155 UK: Market, by Offering, 2023-2028 (USD Million)
Table 156 UK: Market, by Type, 2017-2022 (USD Million)
Table 157 UK: Market, by Type, 2023-2028 (USD Million)
Table 158 UK: Market, by Deployment, 2017-2022 (USD Million)
Table 159 UK: Market, by Deployment, 2023-2028 (USD Million)
Table 160 UK: Market, by Service, 2017-2022 (USD Million)
Table 161 UK: Market, by Service, 2023-2028 (USD Million)
9.3.4 Germany
9.3.4.1 Strong It Infrastructure and Robust Regulatory Framework to Drive Automl Market in Germany
9.3.5 France
9.3.5.1 Country's Thriving Startup Ecosystem to Boost Adoption of Automated Machine Learning Solutions
9.3.6 Italy
9.3.6.1 Significant Initiatives Taken by Government to Promote the Use of Automated Machine Learning Platforms to Boost Market Growth
9.3.7 Spain
9.3.7.1 Rising Technological Investments by Major Players to Boost Popularity of Automl Platforms and Solutions in Spain
9.3.8 Nordic
9.3.8.1 Increasing Research and Development in Ai and Machine Learning in Nordic Countries to Drive Market Growth
9.3.9 Rest of Europe
9.4 Asia-Pacific
9.4.1 Asia-Pacific: Market Drivers
9.4.2 Asia-Pacific: Recession Impact
Figure 37 Asia-Pacific: Automated Machine Learning Market Snapshot
Table 162 Asia-Pacific: Market, by Offering, 2017-2022 (USD Million)
Table 163 Asia-Pacific: Market, by Offering, 2023-2028 (USD Million)
Table 164 Asia-Pacific: Market, by Type, 2017-2022 (USD Million)
Table 165 Asia-Pacific: Market, by Type, 2023-2028 (USD Million)
Table 166 Asia-Pacific: Market, by Deployment, 2017-2022 (USD Million)
Table 167 Asia-Pacific: Market, by Deployment, 2023-2028 (USD Million)
Table 168 Asia-Pacific: Market, by Service, 2017-2022 (USD Million)
Table 169 Asia-Pacific: Market, by Service, 2023-2028 (USD Million)
Table 170 Asia-Pacific: Market, by Application, 2017-2022 (USD Million)
Table 171 Asia-Pacific: Market, by Application, 2023-2028 (USD Million)
Table 172 Asia-Pacific: Market, by Vertical, 2017-2022 (USD Million)
Table 173 Asia-Pacific: Market, by Vertical, 2023-2028 (USD Million)
Table 174 Asia-Pacific: Market, by Country, 2017-2022 (USD Million)
Table 175 Asia-Pacific: Market, by Country, 2023-2028 (USD Million)
9.4.3 China
9.4.3.1 Heavy Investments Made in Machine Learning Technology to Drive Growth of Automated Machine Learning Solutions in China
Table 176 China: Automated Machine Learning Market, by Offering, 2017-2022 (USD Million)
Table 177 China: Market, by Offering, 2023-2028 (USD Million)
Table 178 China: Market, by Type, 2017-2022 (USD Million)
Table 179 China: Market, by Type, 2023-2028 (USD Million)
Table 180 China: Market, by Deployment, 2017-2022 (USD Million)
Table 181 China: Market, by Deployment, 2023-2028 (USD Million)
Table 182 China: Market, by Service, 2017-2022 (USD Million)
Table 183 China: Market, by Service, 2023-2028 (USD Million)
9.4.4 Japan
9.4.4.1 Growing Need for Technological Enhancements to Boost Growth of Automl Solutions and Services in Japan
9.4.5 South Korea
9.4.5.1 Strong Focus on Developing Cutting-Edge Technologies to Boost Use of Automl Solutions Across Sectors in South Korea
9.4.6 Asean
9.4.6.1 Rising Demand to Leverage Machine Learning Solutions for Competitive Advantage to Boost Growth of Market
9.4.7 Australia & New Zealand
9.4.7.1 Increased Innovations by Major Companies Specializing in Machine Learning to Drive Adoption of Automl Solutions Across Industries
9.4.8 Rest of Asia-Pacific
9.5 Middle East & Africa
9.5.1 Middle East & Africa: Market Drivers
9.5.2 Middle East & Africa: Recession Impact
Table 184 Middle East & Africa: Automated Machine Learning Market, by Offering, 2017-2022 (USD Million)
Table 185 Middle East & Africa: Market, by Offering, 2023-2028 (USD Million)
Table 186 Middle East & Africa: Market, by Type, 2017-2022 (USD Million)
Table 187 Middle East & Africa: Market, by Type, 2023-2028 (USD Million)
Table 188 Middle East & Africa: Market, by Deployment, 2017-2022 (USD Million)
Table 189 Middle East & Africa: Market, by Deployment, 2023-2028 (USD Million)
Table 190 Middle East & Africa: Market, by Service, 2017-2022 (USD Million)
Table 191 Middle East & Africa: Market, by Service, 2023-2028 (USD Million)
Table 192 Middle East & Africa: Market, by Application, 2017-2022 (USD Million)
Table 193 Middle East & Africa: Market, by Application, 2023-2028 (USD Million)
Table 194 Middle East & Africa: Market, by Vertical, 2017-2022 (USD Million)
Table 195 Middle East & Africa: Market, by Vertical, 2023-2028 (USD Million)
Table 196 Middle East & Africa: Market, by Country, 2017-2022 (USD Million)
Table 197 Middle East & Africa: Market, by Country, 2023-2028 (USD Million)
9.5.3 Saudi Arabia
9.5.3.1 Saudi Arabia's Commitment to Leveraging Ai and Ml Technologies to Drive Market Growth
9.5.4 UAE
9.5.4.1 Rising Growth of Advanced Technologies to Drive Market for Ai and Ml Solutions and Services
9.5.5 Israel
9.5.5.1 Growing Investments in Ai and Ml Research by Major Players to Boost Growth of Market in Israel
9.5.6 Turkey
9.5.6.1 Growing Ecosystem and Adoption of Machine Learning Technology Across Industries to Boost Market Growth in Turkey
9.5.7 South Africa
9.5.7.1 Increasing Investments and Initiatives from Governments and Private Sector to Drive Popularity of Ai and Ml Solutions
9.5.8 Rest of Middle East & Africa
9.6 Latin America
9.6.1 Latin America: Automl Market Drivers
9.6.2 Latin America: Recession Impact
Table 198 Latin America: Automated Machine Learning Market, by Offering, 2017-2022 (USD Million)
Table 199 Latin America: Market, by Offering, 2023-2028 (USD Million)
Table 200 Latin America: Market, by Type, 2017-2022 (USD Million)
Table 201 Latin America: Market, by Type, 2023-2028 (USD Million)
Table 202 Latin America: Market, by Deployment, 2017-2022 (USD Million)
Table 203 Latin America: Market, by Deployment, 2023-2028 (USD Million)
Table 204 Latin America: Market, by Service, 2017-2022 (USD Million)
Table 205 Latin America: Market, by Service, 2023-2028 (USD Million)
Table 206 Latin America: Market, by Application, 2017-2022 (USD Million)
Table 207 Latin America: Market, by Application, 2023-2028 (USD Million)
Table 208 Latin America: Market, by Vertical, 2017-2022 (USD Million)
Table 209 Latin America: Market, by Vertical, 2023-2028 (USD Million)
Table 210 Latin America: Market, by Country, 2017-2022 (USD Million)
Table 211 Latin America: Market, by Country, 2023-2028 (USD Million)
9.6.3 Brazil
9.6.3.1 Significant Government Support to Drive Adoption of Ai and Ml Technologies Across Industries
9.6.4 Mexico
9.6.4.1 Rapid Growth in Country's Technology Sector to Drive Market for Automated Machine Learning
9.6.5 Argentina
9.6.5.1 Government Incentives to Foreign Companies for Investments in Country's Technology Sector to Boost Automl Market Growth
9.6.6 Rest of Latin America

10 Competitive Landscape
10.1 Overview
10.2 Strategies Adopted by Key Players
Table 212 Strategies Adopted by Key Players
10.3 Revenue Analysis
Figure 38 Revenue Analysis for Key Players, 2018-2022
10.4 Market Share Analysis
Figure 39 Market Share Analysis for Key Players, 2022
Table 213 Automated Machine Learning Market: Intensity of Competitive Rivalry
10.5 Evaluation Quadrant Matrix for Key Players
10.5.1 Stars
10.5.2 Emerging Leaders
10.5.3 Pervasive Players
10.5.4 Participants
Figure 40 Evaluation Quadrant Matrix for Key Players, 2023
10.6 Evaluation Quadrant Matrix for Smes/Startups
10.6.1 Progressive Companies
10.6.2 Responsive Companies
10.6.3 Dynamic Companies
10.6.4 Starting Blocks
Figure 41 Evaluation Quadrant Matrix for Smes/Startups, 2023
10.7 Competitive Benchmarking
Table 214 Competitive Benchmarking for Key Players, 2023
Table 215 Detailed List of Key SMEs/Startups
Table 216 Competitive Benchmarking for Smes/Startups, 2023
10.8 Automated Machine Learning Product Landscape
10.8.1 Comparative Analysis of Automated Machine Learning Products
Table 217 Comparative Analysis of Automated Machine Learning Products
Figure 42 Comparative Analysis of Automated Machine Learning Products
10.9 Competitive Scenario
10.9.1 Product Launches
Table 218 Market: Product Launches, 2020-2023
10.9.2 Deals
Table 219 Market: Deals, 2020-2023
10.9.3 Others
Table 220 Automated Machine Learning Market: Others, 2020-2022
10.10 Valuation and Financial Metrics of Key Automated Machine Learning Vendors
Figure 43 Valuation and Financial Metrics of Key Automated Machine Learning Vendors
10.11 Ytd Price Total Return and Stock Beta of Key Automated Machine Learning Vendors
Figure 44 Ytd Price Total Return and Stock Beta of Key Automated Machine Learning Vendors

11 Company Profiles
11.1 Introduction
11.2 Key Players
(Business Overview, Products/Solutions Offered, Recent Developments, Analyst's View)*
11.2.1 IBM
Table 221 IBM: Business Overview
Figure 45 IBM: Company Snapshot
Table 222 IBM: Products/Solutions Offered
Table 223 IBM: Product Launches
Table 224 IBM: Deals
11.2.2 Oracle
Table 225 Oracle: Business Overview
Figure 46 Oracle: Company Snapshot
Table 226 Oracle: Products/Solutions Offered
Table 227 Oracle: Product Launches
Table 228 Oracle: Deals
Table 229 Oracle: Others
11.2.3 Microsoft
Table 230 Microsoft: Business Overview
Figure 47 Microsoft: Company Snapshot
Table 231 Microsoft: Products/Solutions Offered
Table 232 Microsoft: Product Launches
Table 233 Microsoft: Deals
11.2.4 ServiceNow
Table 234 Servicenow: Business Overview
Figure 48 Servicenow: Company Snapshot
Table 235 Servicenow: Products/Solutions Offered
Table 236 Servicenow: Product Launches
Table 237 Servicenow: Deals
11.2.5 Google
Table 238 Google: Business Overview
Figure 49 Google: Company Snapshot
Table 239 Google: Products/Solutions Offered
Table 240 Google: Product Launches
Table 241 Google: Deals
11.2.6 Baidu
Table 242 Baidu: Business Overview
Figure 50 Baidu: Company Snapshot
Table 243 Baidu: Products Offered
Table 244 Baidu: Product Launches
Table 245 Baidu: Deals
11.2.7 Aws
Table 246 Aws: Business Overview
Figure 51 Aws: Company Snapshot
Table 247 Aws: Products/Services Offered
Table 248 Aws: Product Launches
Table 249 Aws: Deals
Table 250 Aws: Others
11.2.8 Alteryx
Table 251 Alteryx: Business Overview
Figure 52 Alteryx: Company Snapshot
Table 252 Alteryx: Products Offered
Table 253 Alteryx: Product Launches
Table 254 Alteryx: Deals
11.2.9 Hpe
Table 255 Hpe: Business Overview
Figure 53 Hpe: Company Snapshot
Table 256 Hpe: Products/Solutions Offered
Table 257 Hpe: Product Launches
Table 258 Hpe: Deals
11.2.10 Salesforce
Table 259 Salesforce: Business Overview
Figure 54 Salesforce: Company Snapshot
Table 260 Salesforce: Products/Solutions Offered
Table 261 Salesforce: Product Launches
Table 262 Salesforce: Deals
11.2.11 Altair
Table 263 Altair: Business Overview
Figure 55 Altair: Company Snapshot
Table 264 Altair: Products/Solutions Offered
Table 265 Altair: Product Launches
Table 266 Altair: Deals
11.2.12 Teradata
Table 267 Teradata: Business Overview
Figure 56 Teradata: Company Snapshot
Table 268 Teradata: Products/Solutions Offered
Table 269 Teradata: Deals
11.2.13 H2O.Ai
Table 270 H2O.Ai: Business Overview
Table 271 H2O.Ai: Products/Solutions Offered
Table 272 H2O.Ai: Product Launches
Table 273 H2O.Ai: Deals
11.2.14 Datarobot
Table 274 Datarobot: Business Overview
Table 275 Datarobot: Products/Services Offered
Table 276 Datarobot: Deals
11.2.15 Bigml
Table 277 Bigml: Business Overview
Table 278 Bigml: Products/Solutions Offered
Table 279 Bigml: Product Launches
Table 280 Bigml: Deals
11.2.16 Databricks
Table 281 Databricks: Business Overview
Table 282 Databricks: Products/Solutions Offered
Table 283 Databricks: Product Launches
Table 284 Databricks: Deals
11.2.17 Dataiku
Table 285 Dataiku: Business Overview
Table 286 Dataiku: Products/Solutions Offered
Table 287 Dataiku: Product Launches
Table 288 Dataiku: Deals
11.2.18 Mathworks
Table 289 Mathworks: Business Overview
Table 290 Mathworks: Products/Solutions Offered
Table 291 Mathworks: Product Launches
Table 292 Mathworks: Deals
11.2.19 Sparkcognition
Table 293 Sparkcognition: Business Overview
Table 294 Sparkcognition: Products/Solutions Offered
Table 295 Sparkcognition: Product Launches
Table 296 Sparkcognition: Deals
11.2.20 Qlik
Table 297 Qlik: Business Overview
Table 298 Qlik: Products/Solutions Offered
Table 299 Qlik: Product Launches
Table 300 Qlik: Deals
*Details on Business Overview, Products/Solutions Offered, Recent Developments, and Analyst's View Might Not be Captured in the Case of Unlisted Companies.
11.3 Other Players
11.3.1 Alibaba Cloud
11.3.2 Appier
11.3.3 Squark
11.3.4 Aible
11.3.5 Datafold
11.3.6 Boost.Ai
11.3.7 Tazi Ai
11.3.8 Akkio
11.3.9 Valohai
11.3.10 Dotdata

12 Adjacent and Related Markets
12.1 Generative Ai Market
12.1.1 Market Definition
12.1.2 Market Overview
Table 301 Global Generative Ai Market Size and Growth Rate, 2019-2022 (USD Million, Y-O-Y %)
Table 302 Global Generative Ai Market Size and Growth Rate, 2023-2028 (USD Million, Y-O-Y %)
12.1.3 Generative Ai Market, by Offering
Table 303 Generative Ai Market, by Offering, 2019-2022 (USD Million)
Table 304 Generative Ai Market, by Offering, 2023-2028 (USD Million)
12.1.4 Generative Ai Market, by Application
Table 305 Generative Ai Market, by Application, 2019-2022 (USD Million)
Table 306 Generative Ai Market, by Application, 2023-2028 (USD Million)
12.1.5 Generative Ai Market, by Vertical
Table 307 Generative Ai Market, by Vertical, 2019-2022 (USD Million)
Table 308 Generative Ai Market, by Vertical, 2023-2028 (USD Million)
12.1.6 Generative Ai Market, by Region
Table 309 Generative Ai Market, by Region, 2019-2022 (USD Million)
Table 310 Generative Ai Market, by Region, 2023-2028 (USD Million)
12.2 Artificial Intelligence Market
12.2.1 Market Definition
12.2.2 Market Overview
12.2.3 Artificial Intelligence Market, by Offering
Table 311 Artificial Intelligence Market, by Offering, 2016-2021 (USD Billion)
Table 312 Artificial Intelligence Market, by Offering, 2022-2027 (USD Billion)
12.2.4 Artificial Intelligence Market, by Technology
Table 313 Artificial Intelligence Market, by Technology, 2016-2021 (USD Billion)
Table 314 Artificial Intelligence Market, by Technology, 2022-2027 (USD Billion)
12.2.5 Artificial Intelligence Market, by Deployment Mode
Table 315 Artificial Intelligence Market, by Deployment Mode, 2016-2021 (USD Billion)
Table 316 Artificial Intelligence Market, by Deployment Mode, 2022-2027 (USD Billion)
12.2.6 Artificial Intelligence Market, by Organization Size
Table 317 Artificial Intelligence Market, by Organization Size, 2016-2021 (USD Billion)
Table 318 Artificial Intelligence Market, by Organization Size, 2022-2027 (USD Billion)
12.2.7 Artificial Intelligence Market, by Business Function
Table 319 Artificial Intelligence Market, by Business Function, 2016-2021 (USD Billion)
Table 320 Artificial Intelligence Market, by Business Function, 2022-2027 (USD Billion)
12.2.8 Artificial Intelligence Market, by Vertical
Table 321 Artificial Intelligence Market, by Vertical, 2016-2021 (USD Billion)
Table 322 Artificial Intelligence Market, by Vertical, 2022-2027 (USD Billion)
12.2.9 Artificial Intelligence Market, by Region
Table 323 Artificial Intelligence Market, by Region, 2016-2021 (USD Billion)
Table 324 Artificial Intelligence Market, by Region, 2022-2027 (USD Billion)

13 Appendix
13.1 Discussion Guide
13.2 Knowledgestore: The Subscription Portal
13.3 Customization Options

Executive Summary

Companies Mentioned

  • Aible
  • Akkio
  • Alibaba Cloud
  • Altair
  • Alteryx
  • Appier
  • AWS
  • Baidu
  • Bigml
  • Boost.AI
  • Databricks
  • Datafold
  • Dataiku
  • Datarobot
  • Dotdata
  • Google
  • H2O.AI
  • HPE
  • IBM
  • Mathworks
  • Microsoft
  • Oracle
  • Qlik
  • Salesforce
  • ServiceNow
  • Sparkcognition
  • Squark
  • Tazi Ai
  • Teradata
  • Valohai

Methodology

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Table Information

Report AttributeDetails
No. of Pages349
PublishedMay 2023
Forecast Period2023 - 2028
Estimated Market Value ( USD ) in 2023 $ 1 Billion
Forecasted Market Value ( USD ) by 2028 $ 6.4 Billion
Compound Annual Growth Rate44.6%
Regions CoveredGlobal
No. of Companies Mentioned30

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About the Autonomous Learning Market

The Autonomous Learning market is a subset of the Machine Learning and Data Mining industry. It focuses on the development of algorithms and systems that can learn from data without requiring explicit programming. Autonomous Learning systems are designed to improve their performance over time, as they are exposed to more data. This technology is used in a variety of applications, such as natural language processing, computer vision, robotics, and autonomous vehicles. Autonomous Learning is a rapidly growing field, with many companies investing in research and development. Companies such as Google, Microsoft, IBM, Amazon, and Apple are leading the way in this space, offering products and services that leverage Autonomous Learning technology. Other companies such as NVIDIA, Intel, and Qualcomm are also investing heavily in this area. Show Less Read more

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