Automated Machine Learning (AutoML) Market

Automated Machine Learning (AutoML) Market Size

             2020                         2024-2032      

Report	Details
Market Size Value	USD 1.2 billion by 2024
Market Size Value	USD 33.0 billion by 2032
CAGR	CAGR of 44.50%
Forecast Period	2024-2032
Base Year	2023
Historic Data	2020
Segments Covered	Offerings, Application, Vertical
Geographics Covered	North America, Latin America, Europe, Asia-Pacific, Middle East & Africa

Major vendors in the global Automated Machine Learning (AutoML) Market are IBM, Oracle, Microsoft, ServiceNow, Google, Baidu, AWS, Alteryx, Salesforce, Altair, Teradata, H2O.ai, DataRobot, BigML, Databricks  , Dataiku, Alibaba Cloud, Appier, Squark, Aible, Datafold  , Boost.ai, Tazi.ai, Akkio, Valohai, dotData, Qlik, Mathworks, HPE, and SparkCognition. And Others.

Market Overview

The Automated Machine Learning (AutoML) market is experiencing significant growth and evolution, driven by the escalating demand for sophisticated machine learning solutions across diverse industries. AutoML, a subset of artificial intelligence, is designed to streamline and automate the complex process of developing machine learning models, making it accessible to a broader audience with varying levels of technical expertise. The market is witnessing increased adoption as organizations recognize the importance of data-driven decision-making in gaining a competitive edge. The rising complexity of machine learning tasks, coupled with a shortage of skilled data scientists, further propels the demand for AutoML solutions. Key players in the market are innovating to provide user-friendly platforms that automate tasks such as data preprocessing, feature engineering, model selection, and hyperparameter tuning. With the democratization of machine learning capabilities, the AutoML market is poised for continued expansion, offering businesses a powerful tool to harness the potential of data analytics for strategic insights and operational efficiency.

Increasing demand for data-driven decision-making

The increasing demand for data-driven decision-making is a key driver for the Automated Machine Learning (AutoML) market. As organizations across various industries recognize the critical role of data in gaining a competitive edge, there is a growing need for effective and efficient tools to harness the power of machine learning. AutoML addresses this demand by automating the end-to-end process of developing machine learning models, enabling individuals with varying levels of technical expertise to leverage the benefits of data-driven insights. The automation of tasks such as feature engineering, model selection, and hyperparameter tuning not only accelerates the model development process but also democratizes access to advanced analytics, making it more accessible to a broader range of professionals within an organization. This trend is expected to fuel the growth of the AutoML market as businesses increasingly prioritize data-driven decision-making for strategic advantages in today's competitive landscape.

Market Dynamics

Drivers:

• Growing adoption of cloud computing
• Increasing Demand for Efficient Fraud Detection Solutions.

Opportunities:

• Developing AutoML solutions for reinforcement learning (RL)
• Developing AutoML solutions for natural language processing (NLP)
• Increasing Democratizing machine learning (ML).

AutoML for explainable AI (XAI)

In the Automated Machine Learning (AutoML) market, a noteworthy trend gaining momentum is the integration of explainable AI (XAI) capabilities. As organizations increasingly rely on machine learning models for critical decision-making, there is a growing emphasis on understanding and interpreting the outcomes of these models. Explainable AI addresses the need for transparency and interpretability in machine learning algorithms, enabling users to comprehend how a model reaches specific conclusions. AutoML platforms are incorporating XAI techniques to enhance the interpretability of the automated models they generate. This trend is driven by the recognition that, especially in regulated industries or mission-critical applications, the ability to explain model predictions is essential for gaining trust, ensuring compliance, and facilitating broader adoption of machine learning solutions. As a result, AutoML solutions equipped with explainable AI features are positioned to be a significant driver in the evolving landscape of automated machine learning.

North America dominates the market for Automated Machine Learning (AutoML) Market.

The North American AutoML market is anticipated for significant growth, driven by increased investments from the BFSI, retail, and healthcare sectors. Both the U.S. and Canada are expected to experience substantial inflows of capital into AI technology and machine learning, aiming to automate workflows and empower data scientists to focus on more intricate challenges. While the media, Information and Communication Technology (ICT) sector, and professional services are set to bolster investments in machine learning and AI, the manufacturing, mining, and utilities sectors may be in the early stages of adopting these technologies.

The coming year’s present compelling growth opportunities for AutoML companies, with end-users actively seeking new avenues in automation technologies to gain a competitive edge. According to the Government of Canada, there is a strong emphasis on exploring the potential of artificial intelligence in improving government services, with a commitment to ensuring clear values, ethics, and regulations guide its implementation.

Fraud Detection Segments Is Anticipated To Hold The Largest Market Share During The Forecast Period

Based on the Application segment categorized into Fraud Detection, Sales & Marketing, Management, Medical Testing, and Transport Optimization. The fraud detection segment is poised to hold a significant portion of the AutoML market, primarily driven by the demand for real-time monitoring of suspicious activities. The increasing focus on mitigating unauthorized use of financial services will fuel the adoption of AutoML solutions. The surge in online credit card fraud and the growing volume of transactions via wallets and mobile phones will further boost the need for AutoML tools in fraud detection. The fraud detection segment commands a substantial share in the Automated Machine Learning (AutoML) market due to its critical role in addressing the escalating challenges of unauthorized activities. The prominence of real-time monitoring for identifying and preventing suspicious transactions has become imperative for businesses, driving the adoption of AutoML solutions. As instances of unauthorized use of financial services continue to rise, there is a heightened need for sophisticated and efficient tools to bolster fraud prevention measures.

Segmentations Analysis of Automated Machine Learning (AutoML) Market: -

• By Offerings
  • Platform
  • Service
• Deployment type
  • On-Premises
  • Cloud
• By Application
  • Fraud Detection
  • Sales & Marketing Management
  • Medical Testing
  • Transport Optimization
• By Vertical
  • BFSI
  • IT & Telecom
  • Healthcare
  • Government
  • Retail
  • Manufacturing
  • Others
• By Region
  • North America
    • US
    • Canada
  • Latin America
    • Brazil
    • Mexico
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of Latin America
  • Europe
    • UK
    • Germany
    • France
    • Italy
    • Spain
    • Russia
    • BENELUX
    • CIS & Russia
    • Nordics
    • Austria
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Thailand
    • Indonesia
    • Malaysia
    • Vietnam
    • Australia & New Zealand
    • Rest of Asia Pacific
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • South Africa
    • Nigeria
    • Egypt
    • Israel
    • Turkey
    • Rest of Middle East & Africa

Recent Developments

• February 2023, IBM has incorporated StepZen's technology into its suite of offerings, seeking to deliver a comprehensive solution to clients for the creation, connection, and administration of APIs and data sources. This integration empowers clients to accelerate innovation and derive enhanced value from their data through a seamless and end-to-end approach.
• February 2023, AWS launched new features for Amazon SageMaker Autopilot, a tool for automating the machine learning (ML) model creation process. The new features include the ability to select specific algorithms for the training and experiment stages, allowing data scientists more control over the ML model creation process.

Automated Machine Learning (AutoML) Market Report Gives Answers to Following Key Questions:

• What will be the Automated Machine Learning (AutoML) Market’s Trends & growth rate? What analysis has been done of the prices, sales, and volume of the top producers of Automated Machine Learning (AutoML) Market?
• What are the main forces behind the worldwide Automated Machine Learning (AutoML) Market? Which companies dominate the Automated Machine Learning (AutoML) Market?
• Which companies dominate the Automated Machine Learning (AutoML) Market? Which business possibilities, dangers, and tactics did they embrace in the market?
• What are the global Natural Food Colors industry's suppliers' opportunities and dangers in Automated Machine Learning (AutoML) Market?
• What is the Natural Food Colors industry's regional sales, income, and pricing analysis? In the Automated Machine Learning (AutoML) Market, who are the distributors, traders, and resellers?
• What are the main geographic areas for various trades that are anticipated to have astounding expansion over the Automated Machine Learning (AutoML) Market?
• What are the main geographical areas for various industries that are anticipated to observe an astounding expansion in Automated Machine Learning (AutoML) Market?
• What are the dominant revenue-generating regions for Automated Machine Learning (AutoML) Market, as well as regional growth trends?
• By the end of the forecast period, what will the market size and growth rate be?
• What are the main Automated Machine Learning (AutoML) Market trends that are influencing the market's expansion?
• Which key product categories dominate the Automated Machine Learning (AutoML) Market? What are the Automated Machine Learning (AutoML) Market’s main applications?
• In the coming years, which Automated Machine Learning (AutoML) Market technology will dominate the market?

Reason to purchase this Automated Machine Learning (AutoML) Market Report:

• Determine prospective investment areas based on a detailed trend analysis of the global Automated Machine Learning (AutoML) Market over the next years.
• Gain an in-depth understanding of the underlying factors driving demand for different Automated Machine Learning (AutoML) Market segments in the top spending countries across the world and identify the opportunities each offers.
• Strengthen your understanding of the market in terms of demand drivers, industry trends, and the latest technological developments, among others.
• Identify the major channels that are driving the global Automated Machine Learning (AutoML) Market, providing a clear picture of future opportunities that can be tapped, resulting in revenue expansion.
• Channelize resources by focusing on the ongoing programs that are being undertaken by the different countries within the global Automated Machine Learning (AutoML) Market.
• Make correct business decisions based on a thorough analysis of the total competitive landscape of the sector with detailed profiles of the top Automated Machine Learning (AutoML) Market  providers worldwide, including information about their products, alliances, recent contract wins, and financial analysis wherever available.