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

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



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 Application, 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 then 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, 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 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), Salesforce (US), among others in the Automated Machine Learning market.


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 Automated Machine Learning 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 Market Players
Figure 4 Approach 2 – Bottom-up (Supply Side): Collective Revenue From Offerings Of Automated Machine Learning Market Players
Figure 5 Approach 3 – Bottom-up (Supply Side): Revenue And Subsequent Market Estimation From Automated Machine Learning Market Offerings
Figure 6 Approach 4 – Bottom-up (Demand Side): Share Of Automated Machine Learning 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 Global Automated Machine Learning Market
Table 3 Impact Of Recession On Global Automated Machine Learning 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 Automated Machine Learning 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 Automated Machine Learning Market
4.2 Automated Machine Learning Market, By Vertical
Figure 15 Retail & Ecommerce Segment To Account For Largest Share During Forecast Period
4.3 Automated Machine Learning Market, By Region
Figure 16 North America To Account For Largest Share By 2028
4.4 Automated Machine Learning 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 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 Market: Platform Providers
Table 7 Automated Machine Learning Market: Service Providers
Table 8 Automated Machine Learning Market: Technology Providers
Table 9 Automated Machine Learning Market: 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 Automated Machine Learning Market
Figure 27 Automated Machine Learning 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: Automated Machine Learning Market Drivers
Figure 28 Services Segment To Grow At Higher Cagr During Forecast Period
Table 27 Automated Machine Learning Market, By Offering, 2017–2022 (Usd Million)
Table 28 Automated Machine Learning 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: Automated Machine Learning 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: Automated Machine Learning Market, By Type, 2017–2022 (Usd Million)
Table 32 Solutions: Automated Machine Learning 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: Automated Machine Learning Market, By Region, 2017–2022 (Usd Million)
Table 34 Platforms: Automated Machine Learning 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: Automated Machine Learning Market, By Region, 2017–2022 (Usd Million)
Table 36 Software: Automated Machine Learning 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: Automated Machine Learning Market, By Deployment, 2017–2022 (Usd Million)
Table 38 Solutions: Automated Machine Learning 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: Automated Machine Learning Market, By Region, 2017–2022 (Usd Million)
Table 40 On-premises: Automated Machine Learning 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: Automated Machine Learning Market, By Region, 2017–2022 (Usd Million)
Table 42 Cloud: Automated Machine Learning 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: Automated Machine Learning Market, By Type, 2023–2028 (Usd Million)
Table 45 Services: Automated Machine Learning Market, By Region, 2017–2022 (Usd Million)
Table 46 Services: Automated Machine Learning 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: Automated Machine Learning Market, By Region, 2017–2022 (Usd Million)
Table 48 Consulting Services: Automated Machine Learning 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: Automated Machine Learning Market, By Region, 2017–2022 (Usd Million)
Table 50 Deployment And Integration: Automated Machine Learning 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: Automated Machine Learning Market, By Region, 2017–2022 (Usd Million)
Table 52 Training, Support, And Maintenance: Automated Machine Learning Market, By Region, 2023–2028 (Usd Million)
7 Automated Machine Learning Market, By Application
7.1 Introduction
7.1.1 Applications: Automated Machine Learning Market Drivers
Figure 32 Data Processing Segment To Lead Market During Forecast Period
Table 53 Automated Machine Learning Market, By Application, 2017–2022 (Usd Million)
Table 54 Automated Machine Learning 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: Automated Machine Learning Market, By Region, 2017–2022 (Usd Million)
Table 56 Data Processing: Automated Machine Learning 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: Automated Machine Learning Market, By Region, 2017–2022 (Usd Million)
Table 58 Model Selection: Automated Machine Learning 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: Automated Machine Learning 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 Set Of Features For Use In Machine Learning Models To Boost Adoption Of Automl Solutions In Feature Engineering
Table 61 Feature Engineering: Automated Machine Learning Market, By Region, 2017–2022 (Usd Million)
Table 62 Feature Engineering: Automated Machine Learning 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: Automated Machine Learning Market, By Region, 2017–2022 (Usd Million)
Table 66 Other Applications: Automated Machine Learning Market, By Region, 2023–2028 (Usd Million)
8 Automated Machine Learning Market, By Vertical
8.1 Introduction
8.1.1 Verticals: Automated Machine Learning Market Drivers
Figure 33 Bfsi Segment To Account For Larger Market Size During Forecast Period
Table 67 Automated Machine Learning Market, By Vertical, 2017–2022 (Usd Million)
Table 68 Automated Machine Learning 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 Bfsi Sector
Table 69 Bfsi: Use Cases
Table 70 Bfsi: Automated Machine Learning Market, By Region, 2017–2022 (Usd Million)
Table 71 Bfsi: Automated Machine Learning Market, By Region, 2023–2028 (Usd Million)
Table 72 Bfsi: Automated Machine Learning Market, By Sub-vertical, 2017–2022 (Usd Million)
Table 73 Bfsi: Automated Machine Learning 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: Automated Machine Learning Market, By Region, 2023–2028 (Usd Million)
Table 77 Healthcare & Life Sciences: Automated Machine Learning Market, By Sub-vertical, 2017–2022 (Usd Million)
Table 78 Healthcare & Life Sciences: Automated Machine Learning 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 & Ecommerce
8.4.1 Growing Need For Personalization And Optimization In Highly Competitive Industries To Boost Market Growth
Table 79 Retail & Ecommerce: Use Cases
Table 80 Retail & Ecommerce: Automated Machine Learning Market, By Region, 2017–2022 (Usd Million)
Table 81 Retail & Ecommerce: Automated Machine Learning Market, By Region, 2023–2028 (Usd Million)
Table 82 Retail & Ecommerce: Automated Machine Learning Market, By Sub-vertical, 2017–2022 (Usd Million)
Table 83 Retail & Ecommerce: Automated Machine Learning 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: Automated Machine Learning Market, By Region, 2023–2028 (Usd Million)
Table 87 Manufacturing: Automated Machine Learning Market, By Sub-vertical, 2017–2022 (Usd Million)
Table 88 Manufacturing: Automated Machine Learning 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: Automated Machine Learning Market, By Region, 2023–2028 (Usd Million)
Table 92 Government & Defense: Automated Machine Learning Market, By Sub-vertical, 2017–2022 (Usd Million)
Table 93 Government & Defense: Automated Machine Learning 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 Telecommunications Industry
Table 94 Telecommunications: Use Cases
Table 95 Telecommunications: Automated Machine Learning Market, By Region, 2017–2022 (Usd Million)
Table 96 Telecommunications: Automated Machine Learning Market, By Region, 2023–2028 (Usd Million)
Table 97 Telecommunications: Automated Machine Learning Market, By Sub-vertical, 2017–2022 (Usd Million)
Table 98 Telecommunications: Automated Machine Learning 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 Automated Machine Learning 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: Automated Machine Learning Market, By Region, 2023–2028 (Usd Million)
Table 102 It/Ites: Automated Machine Learning Market, By Sub-vertical, 2017–2022 (Usd Million)
Table 103 It/Ites: Automated Machine Learning 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: Automated Machine Learning Market, By Region, 2023–2028 (Usd Million)
Table 107 Automotive, Transportation, And Logistics: Automated Machine Learning Market, By Sub-vertical, 2017–2022 (Usd Million)
Table 108 Automotive, Transportation, And Logistics: Automated Machine Learning 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: Automated Machine Learning Market, By Region, 2023–2028 (Usd Million)
Table 112 Media & Entertainment: Automated Machine Learning Market, By Sub-vertical, 2017–2022 (Usd Million)
Table 113 Media & Entertainment: Automated Machine Learning 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: Automated Machine Learning Market, By Region, 2017–2022 (Usd Million)
Table 115 Other Verticals: Automated Machine Learning 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 Automated Machine Learning Market, By Region, 2017–2022 (Usd Million)
Table 117 Automated Machine Learning Market, By Region, 2023–2028 (Usd Million)
9.2 North America
9.2.1 North America: Automated Machine Learning Market Drivers
9.2.2 North America: Recession Impact
Figure 36 North America: Market Snapshot
Table 118 North America: Automated Machine Learning Market, By Offering, 2017–2022 (Usd Million)
Table 119 North America: Automated Machine Learning Market, By Offering, 2023–2028 (Usd Million)
Table 120 North America: Automated Machine Learning Market, By Type, 2017–2022 (Usd Million)
Table 121 North America: Automated Machine Learning Market, By Type, 2023–2028 (Usd Million)
Table 122 North America: Automated Machine Learning Market, By Deployment, 2017–2022 (Usd Million)
Table 123 North America: Automated Machine Learning Market, By Deployment, 2023–2028 (Usd Million)
Table 124 North America: Automated Machine Learning Market, By Service, 2017–2022 (Usd Million)
Table 125 North America: Automated Machine Learning Market, By Service, 2023–2028 (Usd Million)
Table 126 North America: Automated Machine Learning Market, By Application, 2017–2022 (Usd Million)
Table 127 North America: Automated Machine Learning Market, By Application, 2023–2028 (Usd Million)
Table 128 North America: Automated Machine Learning Market, By Vertical, 2017–2022 (Usd Million)
Table 129 North America: Automated Machine Learning Market, By Vertical, 2023–2028 (Usd Million)
Table 130 North America: Automated Machine Learning Market, By Country, 2017–2022 (Usd Million)
Table 131 North America: Automated Machine Learning 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: Automated Machine Learning Market, By Offering, 2023–2028 (Usd Million)
Table 134 Us: Automated Machine Learning Market, By Type, 2017–2022 (Usd Million)
Table 135 Us: Automated Machine Learning Market, By Type, 2023–2028 (Usd Million)
Table 136 Us: Automated Machine Learning Market, By Deployment, 2017–2022 (Usd Million)
Table 137 Us: Automated Machine Learning Market, By Deployment, 2023–2028 (Usd Million)
Table 138 Us: Automated Machine Learning Market, By Service, 2017–2022 (Usd Million)
Table 139 Us: Automated Machine Learning 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: Automated Machine Learning Market Drivers
9.3.2 Europe: Recession Impact
Table 140 Europe: Automated Machine Learning Market, By Offering, 2017–2022 (Usd Million)
Table 141 Europe: Automated Machine Learning Market, By Offering, 2023–2028 (Usd Million)
Table 142 Europe: Automated Machine Learning Market, By Type, 2017–2022 (Usd Million)
Table 143 Europe: Automated Machine Learning Market, By Type, 2023–2028 (Usd Million)
Table 144 Europe: Automated Machine Learning Market, By Deployment, 2017–2022 (Usd Million)
Table 145 Europe: Automated Machine Learning Market, By Deployment, 2023–2028 (Usd Million)
Table 146 Europe: Automated Machine Learning Market, By Service, 2017–2022 (Usd Million)
Table 147 Europe: Automated Machine Learning Market, By Service, 2023–2028 (Usd Million)
Table 148 Europe: Automated Machine Learning Market, By Application, 2017–2022 (Usd Million)
Table 149 Europe: Automated Machine Learning Market, By Application, 2023–2028 (Usd Million)
Table 150 Europe: Automated Machine Learning Market, By Vertical, 2017–2022 (Usd Million)
Table 151 Europe: Automated Machine Learning Market, By Vertical, 2023–2028 (Usd Million)
Table 152 Europe: Automated Machine Learning Market, By Country, 2017–2022 (Usd Million)
Table 153 Europe: Automated Machine Learning Market, By Country, 2023–2028 (Usd Million)
Table 154 Uk: Automated Machine Learning Market, By Offering, 2017–2022 (Usd Million)
Table 155 Uk: Automated Machine Learning Market, By Offering, 2023–2028 (Usd Million)
Table 156 Uk: Automated Machine Learning Market, By Type, 2017–2022 (Usd Million)
Table 157 Uk: Automated Machine Learning Market, By Type, 2023–2028 (Usd Million)
Table 158 Uk: Automated Machine Learning Market, By Deployment, 2017–2022 (Usd Million)
Table 159 Uk: Automated Machine Learning Market, By Deployment, 2023–2028 (Usd Million)
Table 160 Uk: Automated Machine Learning Market, By Service, 2017–2022 (Usd Million)
Table 161 Uk: Automated Machine Learning 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 Use Of Automated Machine Learning Platforms To Boost Market Growth
9.3.7 Spain

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