Automated Machine Learning (AutoML) Markets: Data Processing, Model Selection, Hyperparameter Optimization & Tuning, Feature Engineering, Model Ensembling - Global Forecast to 2028

Dublin, Oct. 16, 2023 (GLOBE NEWSWIRE) -- The "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" report has been added to's offering.

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%

This market study delves into Automated Machine Learning (AutoML) across various segments, aiming to gauge market size and growth potential within categories like offering, application, vertical, and geographical regions.

The analysis includes a comprehensive competitive assessment of key market players, encompassing their company profiles, noteworthy insights related to product and business offerings, recent advancements, and key market strategies.

Explainable AI holds significant importance within the realm of AutoML as it strives to introduce transparency into the decision-making processes of machine learning models. Utilizing explainable AI techniques, such as feature importance and decision trees, businesses can gain valuable insights into the inner workings of their models, facilitating more informed decision-making.

North America is anticipated to hold the largest share of the Automated Machine Learning market. The global Automated Machine Learning market is predominantly led by North America, with the region being the highest revenue-generating area. Within this region, the United States takes the lion's share of the market, followed by Canada.

North America boasts a high adoption rate of machine learning and artificial intelligence technologies across diverse industries, including healthcare, finance, and retail. This widespread adoption is expected to drive the demand for AutoML solutions. Additionally, the region is home to a multitude of data-driven startups and companies, further propelling the growth of the AutoML market in North America.

The report also provides a comprehensive evaluation of market shares, growth strategies, and service portfolios of leading players such as IBM (US), Google (US), AWS(US), Microsoft (US), and Salesforce (US), among others, in the Automated Machine Learning market.

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.

Key Attributes:

Report AttributeDetails
No. of Pages349
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

Premium Insights

  • Rising Demand for Platforms to Transfer Data from On-Premises to Cloud to Drive Learning Market
  • Retail & E-commerce Segment to Account for Largest Share During Forecast Period
  • North America to Account for Largest Share by 2028
  • Solutions and Bfsi Segments to Account for Significant Share by 2028

Case Study Analysis

Real Estate

  • Ascendas Singbridge Group Improved Real Estate Decision-Making by Leveraging Datarobot's Automl Platform
  • G5 Employed H2O.Ai's Driverless Ai Platform to Address Challenges in Identifying Productive Leads


  • Robotica Helped Avant Automate Key Processes and Streamline Lending Operations
  • Domestic and General Partnered with Datarobot to Improve Customer Service Capabilities
  • H2O.Ai's Machine Learning Platform Enabled Paypal to Strengthen Fraud Detection Capabilities

Retail & Ecommerce

  • California Design Den Partnered with Google Cloud Platform to Implement Machine Learning Solutions


  • Contentree Helped Consensus Simplify Data Wrangling Process and Make It Efficient
  • Datarobot's Automated Machine Learning Platform Helped Demyst Automate Data Science Processes

Healthcare & Lifesciences

  • Datarobot Helped Evariant Automate Patient Risk Stratification and Readmission Prediction

Media & Entertainment

  • Meredith Corporation Worked with Google Cloud to Build Data Analytics Platform to Handle Large Volumes of Data

Transportation & Logistics

  • Dmway Enabled Pgl to Integrate and Analyze Data from Multiple Sources

Energy & Utilities

  • Sparkcognition Helped Oil & Gas Industry to Build Predictive Models by Leveraging Automated Machine Learning Solutions

Market Dynamics


  • Growing Demand for Improved Customer Satisfaction and Personalized Product Recommendations Through Automl
  • Increasing Need for Accurate Fraud Detection
  • Growing Data Volume and Complexity
  • Rising to Need to Transform Businesses with Intelligent Automation Using Automl


  • Slow Adoption of Machine Learning Tools
  • Lack of Standardization and Regulations


  • Growing Demand for Ai-Enabled Solutions Across Industries
  • Seamless Integration Between Technologies
  • Increased Accessibility of Machine Learning Solutions


  • Growing Shortage of Skilled Workforce
  • Difficulty in Interpreting and Explaining Automl Models
  • Rising Threat to Data Privacy

Value Chain Analysis

  • Data Collection & Preparation
  • Algorithm Development
  • Model Training
  • Model Testing and Validation
  • Deployment and Integration
  • Maintenance and Support

Business Models of Automl

  • Api Models
  • As-A-Service Model
  • Cloud Model

Technology Analysis

  • Supervised Learning
  • Unsupervised Learning
  • Natural Language Processing
  • Computer Vision
  • Transfer Learning
  • Cloud Computing
  • Robotics
  • Federated Learning

Company Profiles

Key Players

  • IBM
  • Oracle
  • Microsoft
  • ServiceNow
  • Google
  • Baidu
  • Aws
  • Alteryx
  • Hpe
  • Salesforce
  • Altair
  • Teradata
  • H2O.Ai
  • Datarobot
  • Bigml
  • Databricks
  • Dataiku
  • Mathworks
  • Sparkcognition
  • Qlik

Other Players

  • Alibaba Cloud
  • Appier
  • Squark
  • Aible
  • Datafold
  • Boost.Ai
  • Tazi Ai
  • Akkio
  • Valohai
  • Dotdata

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Global Automated Machine Learning (AutoML) Market

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