AutoML Market: Accelerating to 10.38 Billion by 2030

Richmond, United States, 2024-Apr-04 — /EPR Network/ —

The Automated Machine Learning (AutoML) Market, valued at USD 1.16 Billion in 2023, is anticipated to reach USD 10.38 Billion by 2030. This growth projection indicates a substantial compound annual growth rate (CAGR) of 36.76% during the forecast period from 2023 to 2030.

In the era of data-driven decision-making, the demand for machine learning solutions has surged, prompting the rise of automated machine learning (AutoML) technologies. AutoML streamlines and automates the machine learning workflow, democratizing access to powerful predictive analytics tools and enabling organizations to leverage machine learning capabilities without requiring extensive expertise. In this analysis, we delve into the Automated Machine Learning (AutoML) Market, exploring its current landscape, growth projections, key drivers, and market dynamics.

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Major vendors in the global Automated Machine Learning (AutoML) Market are :

  • IBM
  • Oracle
  • Microsoft
  • ServiceNow
  • Google
  • Baidu
  • AWS (Amazon Web Services)
  • Alteryx
  • Salesforce
  • Altair
  • Teradata
  • DataRobot
  • BigML
  • Databricks
  • Dataiku
  • Alibaba Cloud
  • Appier
  • Squark
  • Aible
  • Datafold
  • Akkio
  • Valohai
  • dotData
  • Qlik
  • Mathworks
  • HPE (Hewlett Packard Enterprise)
  • SparkCognition

Defining Automated Machine Learning (AutoML)

Automated Machine Learning (AutoML) refers to the process of automating the end-to-end machine learning pipeline, including data preprocessing, feature engineering, model selection, hyperparameter tuning, and model deployment. AutoML platforms leverage advanced algorithms and techniques to automatically build, train, and optimize machine learning models, enabling users with varying levels of expertise to create predictive models without the need for manual intervention.

Market Size and Growth Projections

The Automated Machine Learning (AutoML) Market has experienced significant growth in recent years, driven by increasing demand for scalable, efficient, and user-friendly machine learning solutions across industries. According to market research reports, the global AutoML market size is projected to reach USD X billion by [year], with a compound annual growth rate (CAGR) of X% during the forecast period [year]-[year]. Factors contributing to market growth include the proliferation of big data, advancements in artificial intelligence (AI) and machine learning (ML) technologies, and the growing need for automation and efficiency in data science workflows.

Key Drivers and Market Dynamics

Several factors are driving the growth of the Automated Machine Learning (AutoML) Market. Firstly, the shortage of data science talent and the complexity of traditional machine learning workflows have created a demand for AutoML platforms that enable non-experts to build and deploy machine learning models effectively. Moreover, the increasing volume, variety, and velocity of data generated by organizations are fueling demand for automated solutions that can handle large-scale data analytics tasks efficiently.

Additionally, the integration of AutoML capabilities into enterprise software platforms, cloud services, and business intelligence tools is democratizing access to machine learning capabilities and accelerating adoption across industries. Furthermore, the emergence of AutoML platforms that offer transparent, explainable, and interpretable machine learning models is addressing concerns regarding model fairness, bias, and accountability, thereby enhancing trust and confidence in automated decision-making systems.

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Market Segmentation and Applications

The Automated Machine Learning (AutoML) Market can be segmented based on deployment mode, organization size, industry vertical, and geographic region. In terms of deployment mode, cloud-based AutoML platforms are witnessing rapid adoption due to their scalability, flexibility, and cost-effectiveness. Moreover, AutoML solutions cater to organizations of all sizes, from small and medium-sized enterprises (SMEs) to large enterprises, across various industry verticals, including healthcare, finance, retail, manufacturing, and telecommunications.

Applications of AutoML span a wide range of use cases, including predictive analytics, forecasting, anomaly detection, customer segmentation, recommendation systems, and natural language processing (NLP). AutoML platforms empower business users, data analysts, and domain experts to leverage machine learning techniques for data-driven decision-making, process optimization, and value creation across the organization.

Challenges and Future Outlook

Despite its rapid growth and adoption, the Automated Machine Learning (AutoML) Market faces certain challenges, including data quality issues, algorithmic biases, and interpretability concerns. Moreover, the black-box nature of some AutoML models may hinder their adoption in regulated industries or applications where transparency and accountability are critical.

However, with ongoing advancements in AI and ML technologies, coupled with efforts to address these challenges, the future outlook for the AutoML market is promising. As organizations continue to prioritize data-driven strategies and digital transformation initiatives, the demand for AutoML solutions that democratize access to machine learning capabilities and enable automation and efficiency in data science workflows is expected to increase significantly.

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.

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In conclusion, the Automated Machine Learning (AutoML) Market is poised for robust growth and innovation, driven by increasing demand for scalable, efficient, and user-friendly machine learning solutions. By democratizing access to machine learning capabilities, AutoML platforms empower organizations to extract actionable insights from data, drive innovation, and gain a competitive edge in today’s data-driven economy. As AutoML continues to evolve and mature, it will play a central role in shaping the future of AI-driven decision-making and unlocking new opportunities for value creation across industries.

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