Automated Machine Learning
Automated Machine Learning Market by Automation Type (Data Processing, Feature Engineering, Modeling), Deployment (Cloud, On-premises), Application - Global Forecast 2024-2030
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[194 Pages Report] The Automated Machine Learning Market size was estimated at USD 1.63 billion in 2023 and expected to reach USD 2.21 billion in 2024, at a CAGR 35.70% to reach USD 13.88 billion by 2030.

Automated machine learning (AutoML) automates the application of machine learning to real-world problems, covering tasks including data preparation, feature engineering, model selection, and parameter tuning. AutoML reduces complexity, enhances productivity, and democratizes AI, making machine learning accessible even to non-experts. Key applications are evident in healthcare for personalized treatments, finance for fraud detection and risk assessment, retail for supply chain optimization and customer personalization, and manufacturing for predictive maintenance and quality control. Influential growth factors include technological advancements in cloud computing and AI, increased data generation, and cost efficiency resulting from reduced need for specialized talent. Leveraging cloud-based AutoML platforms, developing industry-specific applications, and fostering collaborative initiatives with tech companies and academic institutions can present new avenues for the companies. However, challenges include reliance on data quality, ensuring model interpretability, and addressing high initial investment costs and talent gaps. Innovation areas focus on Explainable AI for transparent outcomes, edge computing for real-time analysis, and algorithm advancements to enhance speed and accuracy. The dynamic and competitive AutoML market benefits from continuous technological advancements and increasing data complexity, with widespread acceptance across various industries underscoring the need for accessible analytics tools.

Regional Insights
The United States is central in adopting and developing automated machine learning (AutoML) technologies in the Americas, with key sectors comprising healthcare, finance, and retail driving demand. Canada’s market is expanding, bolstered by a strong tech ecosystem and supportive government initiatives. Major EU countries such as Germany, France, and the U.K. are focusing on digital transformation and stringent data privacy regulations under GDPR. The Middle East and Africa (MEA) region exhibits growing interest, particularly in the UAE and South Africa, driven by digital modernization efforts. China is a significant player, with substantial contributions from the public and private sectors, and prioritizes innovations in e-commerce and manufacturing analytics. Japan focuses on integrating AI with robotics and IoT applications, addressing workforce shortages. India’s IT services industry increasingly incorporates AutoML to enhance service delivery, which is supported by substantial investments in AI startups. Latin America’s AutoML market is led by Brazil and Mexico, particularly in agriculture, finance, and e-commerce. Emerging African markets, including Kenya and Nigeria, are gaining traction with AI accelerators and mobile-based applications. ASEAN countries such as Singapore and Malaysia are actively adopting AutoML for finance, logistics, and healthcare, driven by flexible subscription models to meet diverse business needs.
Automated Machine Learning Market
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Market Dynamics

The market dynamics represent an ever-changing landscape of the Automated Machine Learning Market by providing actionable insights into factors, including supply and demand levels. Accounting for these factors helps design strategies, make investments, and formulate developments to capitalize on future opportunities. In addition, these factors assist in avoiding potential pitfalls related to political, geographical, technical, social, and economic conditions, highlighting consumer behaviors and influencing manufacturing costs and purchasing decisions.

  • Market Drivers
    • Increasing demand for data-driven insights for decision-making
    • Expanding democratization of machine learning capabilities
  • Market Restraints
    • Interpretability and transparency issues associated with AutoML platforms
  • Market Opportunities
    • Advancements in artificial intelligence (AI) and machine learning (ML) technologies
    • Growing integration of AutoML with DevOps practices that enhance the development of machine learning models
  • Market Challenges
    • Security and privacy concerns of AutoML platforms
Market Disruption Analysis

The market disruption analysis delves into the core elements associated with market-influencing changes, including breakthrough technological advancements that introduce novel features, integration capabilities, regulatory shifts that could drive or restrain market growth, and the emergence of innovative market players challenging traditional paradigms. This analysis facilitates a competitive advantage by preparing players in the Automated Machine Learning Market to pre-emptively adapt to these market-influencing changes, enhances risk management by early identification of threats, informs calculated investment decisions, and drives innovation toward areas with the highest demand in the Automated Machine Learning Market.

Porter’s Five Forces Analysis

The porter's five forces analysis offers a simple and powerful tool for understanding, identifying, and analyzing the position, situation, and power of the businesses in the Automated Machine Learning Market. This model is helpful for companies to understand the strength of their current competitive position and the position they are considering repositioning into. With a clear understanding of where power lies, businesses can take advantage of a situation of strength, improve weaknesses, and avoid taking wrong steps. The tool identifies whether new products, services, or companies have the potential to be profitable. In addition, it can be very informative when used to understand the balance of power in exceptional use cases.

Value Chain & Critical Path Analysis

The value chain of the Automated Machine Learning Market encompasses all intermediate value addition activities, including raw materials used, product inception, and final delivery, aiding in identifying competitive advantages and improvement areas. Critical path analysis of the <> market identifies task sequences crucial for timely project completion, aiding resource allocation and bottleneck identification. Value chain and critical path analysis methods optimize efficiency, improve quality, enhance competitiveness, and increase profitability. Value chain analysis targets production inefficiencies, and critical path analysis ensures project timeliness. These analyses facilitate businesses in making informed decisions, responding to market demands swiftly, and achieving sustainable growth by optimizing operations and maximizing resource utilization.

Pricing Analysis

The pricing analysis comprehensively evaluates how a product or service is priced within the Automated Machine Learning Market. This evaluation encompasses various factors that impact the price of a product, including production costs, competition, demand, customer value perception, and changing margins. An essential aspect of this analysis is understanding price elasticity, which measures how sensitive the market for a product is to its price change. It provides insight into competitive pricing strategies, enabling businesses to position their products advantageously in the Automated Machine Learning Market.

Technology Analysis

The technology analysis involves evaluating the current and emerging technologies relevant to a specific industry or market. This analysis includes breakthrough trends across the value chain that directly define the future course of long-term profitability and overall advancement in the Automated Machine Learning Market.

Patent Analysis

The patent analysis involves evaluating patent filing trends, assessing patent ownership, analyzing the legal status and compliance, and collecting competitive intelligence from patents within the Automated Machine Learning Market and its parent industry. Analyzing the ownership of patents, assessing their legal status, and interpreting the patents to gather insights into competitors' technology strategies assist businesses in strategizing and optimizing product positioning and investment decisions.

Trade Analysis

The trade analysis of the Automated Machine Learning Market explores the complex interplay of import and export activities, emphasizing the critical role played by key trading nations. This analysis identifies geographical discrepancies in trade flows, offering a deep insight into regional disparities to identify geographic areas suitable for market expansion. A detailed analysis of the regulatory landscape focuses on tariffs, taxes, and customs procedures that significantly determine international trade flows. This analysis is crucial for understanding the overarching legal framework that businesses must navigate.

Regulatory Framework Analysis

The regulatory framework analysis for the Automated Machine Learning Market is essential for ensuring legal compliance, managing risks, shaping business strategies, fostering innovation, protecting consumers, accessing markets, maintaining reputation, and managing stakeholder relations. Regulatory frameworks shape business strategies and expansion initiatives, guiding informed decision-making processes. Furthermore, this analysis uncovers avenues for innovation within existing regulations or by advocating for regulatory changes to foster innovation.

FPNV Positioning Matrix

The FPNV positioning matrix is essential in evaluating the market positioning of the vendors in the Automated Machine Learning Market. This matrix offers a comprehensive assessment of vendors, examining critical metrics related to business strategy and product satisfaction. This in-depth assessment empowers users to make well-informed decisions aligned with their requirements. Based on the evaluation, the vendors are then categorized into four distinct quadrants representing varying levels of success, namely Forefront (F), Pathfinder (P), Niche (N), or Vital (V).

Market Share Analysis

The market share analysis is a comprehensive tool that provides an insightful and in-depth assessment of the current state of vendors in the Automated Machine Learning Market. By meticulously comparing and analyzing vendor contributions, companies are offered a greater understanding of their performance and the challenges they face when competing for market share. These contributions include overall revenue, customer base, and other vital metrics. Additionally, this analysis provides valuable insights into the competitive nature of the sector, including factors such as accumulation, fragmentation dominance, and amalgamation traits observed over the base year period studied. With these illustrative details, vendors can make more informed decisions and devise effective strategies to gain a competitive edge in the market.

Strategy Analysis & Recommendation

The strategic analysis is essential for organizations seeking a solid foothold in the global marketplace. Companies are better positioned to make informed decisions that align with their long-term aspirations by thoroughly evaluating their current standing in the Automated Machine Learning Market. This critical assessment involves a thorough analysis of the organization’s resources, capabilities, and overall performance to identify its core strengths and areas for improvement.

Key Company Profiles

The report delves into recent significant developments in the Automated Machine Learning Market, highlighting leading vendors and their innovative profiles. These include Aible, Inc., Akkio Inc., Altair Engineering Inc., Alteryx, Amazon Web Services, Inc., Automated Machine Learning Ltd., BigML, Inc., Databricks, Inc., Dataiku, DataRobot, Inc., Google LLC by Alphabet Inc.,, Inc., Hewlett Packard Enterprise Company, InData Labs Group Limited, Intel Corporation, International Business Machines Corporation, Microsoft Corporation, Oracle Corporation, QlikTech International AB, Runai Labs Ltd., Salesforce, Inc., SAS Institute Inc., ServiceNow, Inc., SparkCognition, Inc., STMicroelectronics, Tata Consultancy Services Limited, TAZI AI, Tellius, Inc., Weidmuller Limited, Wolfram, and

Automated Machine Learning Market - Global Forecast 2024-2030
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Market Segmentation & Coverage

This research report categorizes the Automated Machine Learning Market to forecast the revenues and analyze trends in each of the following sub-markets:

  • Automation Type
    • Data Processing
    • Feature Engineering
    • Modeling
    • Visualization
  • Deployment
    • Cloud
    • On-premises
  • Application
    • Automotive, Transportations, and Logistics
    • Banking, Financial Services, and Insurance
    • Government & Defense
    • Healthcare & Life Sciences
    • It & Telecommunications
    • Media & Entertainment

  • Region
    • Americas
      • Argentina
      • Brazil
      • Canada
      • Mexico
      • United States
        • California
        • Florida
        • Illinois
        • New York
        • Ohio
        • Pennsylvania
        • Texas
    • Asia-Pacific
      • Australia
      • China
      • India
      • Indonesia
      • Japan
      • Malaysia
      • Philippines
      • Singapore
      • South Korea
      • Taiwan
      • Thailand
      • Vietnam
    • Europe, Middle East & Africa
      • Denmark
      • Egypt
      • Finland
      • France
      • Germany
      • Israel
      • Italy
      • Netherlands
      • Nigeria
      • Norway
      • Poland
      • Qatar
      • Russia
      • Saudi Arabia
      • South Africa
      • Spain
      • Sweden
      • Switzerland
      • Turkey
      • United Arab Emirates
      • United Kingdom

This research report offers invaluable insights into various crucial aspects of the Automated Machine Learning Market:

  1. Market Penetration: This section thoroughly overviews the current market landscape, incorporating detailed data from key industry players.
  2. Market Development: The report examines potential growth prospects in emerging markets and assesses expansion opportunities in mature segments.
  3. Market Diversification: This includes detailed information on recent product launches, untapped geographic regions, recent industry developments, and strategic investments.
  4. Competitive Assessment & Intelligence: An in-depth analysis of the competitive landscape is conducted, covering market share, strategic approaches, product range, certifications, regulatory approvals, patent analysis, technology developments, and advancements in the manufacturing capabilities of leading market players.
  5. Product Development & Innovation: This section offers insights into upcoming technologies, research and development efforts, and notable advancements in product innovation.

Additionally, the report addresses key questions to assist stakeholders in making informed decisions:

  1. What is the current market size and projected growth?
  2. Which products, segments, applications, and regions offer promising investment opportunities?
  3. What are the prevailing technology trends and regulatory frameworks?
  4. What is the market share and positioning of the leading vendors?
  5. What revenue sources and strategic opportunities do vendors in the market consider when deciding to enter or exit?

Table of Contents
  1. Preface
  2. Research Methodology
  3. Executive Summary
  4. Market Overview
  5. Market Insights
  6. Automated Machine Learning Market, by Automation Type
  7. Automated Machine Learning Market, by Deployment
  8. Automated Machine Learning Market, by Application
  9. Americas Automated Machine Learning Market
  10. Asia-Pacific Automated Machine Learning Market
  11. Europe, Middle East & Africa Automated Machine Learning Market
  12. Competitive Landscape
  13. Competitive Portfolio
  14. List of Figures [Total: 22]
  15. List of Tables [Total: 292]
  16. List of Companies Mentioned [Total: 31]
The Expanding Democratization of Machine Learning Capabilities: Scope and Benefits of Automated Machine Learning
July 3, 2023
The Expanding Democratization of Machine Learning Capabilities: Scope and Benefits of Automated Machine Learning
Machine learning has revolutionized the way we live our lives, from giving us personalized recommendations to predicting natural disasters. However, only a select few individuals who possess technical knowledge and expertise are able to create and deploy machine learning models. The ongoing democratization of this technology has propelled the development of automated machine learning (AutoML). We will delve into the scope and benefits of automated machine learning and explore how it expands the democratization of machine learning capabilities in this blog post.

What is Automated Machine Learning?

Automated Machine Learning (AutoML) is a technique that automates many of the steps involved in developing machine learning models, thus making it easier to create and deploy machine learning models without requiring extensive knowledge of the technicalities involved in building these models. AutoML allows the user to focus on defining the problem statement, selecting appropriate features, and specifying the evaluation metrics. Then, AutoML sets up and trains a machine-learning model based on the specified problem, feature selection, and evaluation metrics.

Scope of Automated Machine Learning:

The scope of automated machine learning is vast, as it allows access to machine learning to a wider audience, including those without deep expertise in the field. AutoML can be used in a variety of domains, from finance to healthcare to e-commerce, enabling businesses to optimize their operations and increase their revenues. Additionally, AutoML can be used to create more interpretability in machine learning models, which will enable businesses to apply ethical considerations into their algorithmic decisions.

Benefits of Automated Machine Learning:

The benefits of using automated machine learning are immense:
AutoML reduces the time it takes to build and deploy machine learning models. With just a few clicks, individuals can create robust models that are optimized for their specific business needs.
AutoML enables non-experts to leverage machine learning capabilities, thus increasing accessibility and democratization of the technology.
AutoML ensures consistency in the creation of machine learning models, as the hyperparameter tuning, algorithm selection, and training are done automatically.
AutoML can optimize machine learning models for interpretability, which is critical for ensuring ethical considerations in algorithmic decision-making.

Limitations of Automated Machine Learning:

Despite the benefits of automated machine learning, there are also some limitations. One disadvantage is that the algorithms generated may not be as powerful as those created by manually tuning the hyperparameters of the model. Additionally, AutoML may not be able to optimize results for specific problems, such as time-series analysis, image recognition, and natural language processing, among others.

Automated machine learning empowers people without deep technical expertise to leverage the power of machine learning and ensure that ethical considerations are incorporated into algorithmic decisions. The scope of AutoML is vast, and it can be used in a variety of domains to optimize operations, increase remuneration, and make more informed decisions. While there are some limitations, AutoML remains a critical tool in the democratization of machine learning capabilities. It is up to businesses to embrace and leverage this technology to drive innovation and create better outcomes.

Frequently Asked Questions
  1. How big is the Automated Machine Learning Market?
    Ans. The Global Automated Machine Learning Market size was estimated at USD 1.63 billion in 2023 and expected to reach USD 2.21 billion in 2024.
  2. What is the Automated Machine Learning Market growth?
    Ans. The Global Automated Machine Learning Market to grow USD 13.88 billion by 2030, at a CAGR of 35.70%
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