Automated Machine Learning (AutoML) Market 2030 By Offering (Solutions, Services), Deployment Mode (Cloud-Based, On-premises), Application (Data Processing, Feature Engineering, Model Selection & Others), Vertical & Region - Partner & Customer Ecosystem (Product Services, Proposition & Key Features) Competitive Index & Regional Footprints by MarketDigits

Industry : Information Technology | Pages : 165 Pages | Published On : Dec 2023

The Global Automated Machine Learning (AutoML) market was valued at USD 1.4 Billion in 2022 and is expected to reach around XX by 2030, at a CAGR of about 43.8% between 2022 and 2030

Major Players In Automated Machine Learning (AutoML) Market Include: IBM Corporation (US), Oracle (US), Microsoft (US), ServiceNow (US), Google LLC. (US), Baidu (China), Amazon Web Services (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) and Others.

Transfer learning plays a crucial role in Automated Machine Learning (AutoML), as it harnesses the power of pre-trained models to enhance the performance of new models. This technique enables businesses to create more accurate models, reducing the need for extensive training data and minimizing the time and cost required to build high-performing models. In today's data-driven business landscape, there is a growing demand for intelligent processes that can enhance decision-making and operational efficiency. These processes leverage machine learning algorithms to automate decision-making and optimize business operations, ultimately leading to improved performance and increased profitability. AutoML serves as a catalyst in this transformation, enabling businesses to streamline operations, cut costs, and elevate performance, thereby gaining a competitive edge. An industry report suggests that AI-driven automation can boost productivity by up to 40%. The Automated Machine Learning Market facilitates such gains by automating the development and deployment of machine learning models. Through AutoML, businesses can swiftly and efficiently craft predictive models, seamlessly integrating them into existing operations to automate decisions, optimize processes, and enhance overall performance. Additionally, AutoML aids in uncovering hidden opportunities for optimization and improvement, previously concealed within vast datasets. By scrutinizing these data volumes, AutoML can identify valuable patterns and trends, empowering businesses to make data-driven decisions for growth.

Despite the numerous advantages AutoML offers, its adoption has been slow, posing a significant challenge to its growth. Organizations often hesitate to embrace this technology, primarily due to a lack of awareness about the Automated Machine Learning (AutoML) Market and its capabilities. A survey by O'Reilly revealed that only 20% of respondents reported using automated machine learning tools, while 48% had never even heard of the technology. This knowledge gap serves as a substantial hurdle to adoption since many business leaders and decision-makers may be unaware of the benefits AutoML can bring to their organizations. Another factor contributing to the sluggish adoption of AutoML is the scarcity of skilled data scientists and machine learning experts. According to an industry expert's report, there could be a shortfall of up to 250,000 data scientists by 2024. This shortage of expertise can hinder organizations' efforts to develop and deploy machine learning models, slowing down adoption rates. Furthermore, concerns regarding the transparency and interpretability of machine learning models add to the resistance. This lack of transparency can be especially problematic in sectors like healthcare and finance, where decisions based on machine learning models carry significant consequences.

The increasing accessibility of machine learning presents a golden opportunity for the AutoML market. Historically, machine learning required a specialized skill set in statistics, programming, and data analysis. However, AutoML tools have democratized machine learning, eliminating the need for extensive teams of data scientists and machine learning experts. These tools enable businesses to make machine learning more accessible across a wider range of use cases and users. With AutoML, businesses can quickly create and deploy predictive models that can analyze large datasets, uncover hidden patterns and insights, which may elude human observation. For instance, AutoML models can predict customer behavior, optimize pricing strategies, and identify opportunities for process enhancement.

Moreover, the increased accessibility of machine learning can translate into substantial cost savings for businesses. By using AutoML tools, organizations can reduce expenses tied to hiring specialized talent and investing in costly infrastructure. Additionally, the faster development and deployment of AI solutions can improve operational efficiency and decision-making, leading to further cost savings. Furthermore, the democratization of machine learning can spur innovation and open up new business opportunities. As more businesses embrace AutoML tools, there is likely to be a surge in new use cases and applications, fostering innovation and market growth. Additionally, democratizing machine learning enables businesses to explore new markets and broaden their offerings, leading to increased revenue and market share. One of the most significant challenges confronting the AutoML market is the shortage of skilled talent. AutoML platforms demand individuals with a solid background in machine learning, data science, and programming. However, the demand for these skills has far exceeded the available talent pool, resulting in a substantial talent deficit in the industry. As a consequence, organizations often struggle to find the right professionals to build, deploy, and maintain AutoML models. According to a LinkedIn report, data scientists and machine learning engineers are among the top emerging jobs in the technology sector. Nonetheless, the scarcity of skilled talent has intensified competition among organizations for a limited pool of candidates. The rapid pace of technological advancements further exacerbates the talent shortage. As new algorithms and techniques emerge, individuals working with AutoML platforms must continuously update their skills to stay current. This necessitates ongoing training and professional development, which can be both expensive and time-consuming. Additionally, the talent deficit extends beyond data scientists and machine learning engineers, encompassing areas such as data management, data visualization, and cloud computing. The shortage of expertise in these domains can also impact the successful implementation and adoption of AutoML solutions.

MARKET DRIVERS

Increasing Demand For AI-Powered Solutions

The rapid expansion of the Global Automated Machine Learning (AutoML) Market can be attributed to the ever-increasing demand for AI[1]powered solutions across industries. Businesses are recognizing the transformative potential of artificial intelligence and machine learning, but not all possess the in-house expertise required to develop and deploy complex models. AutoML addresses this challenge by democratizing machine learning, making it accessible to a broader audience. This growing demand for AI-driven insights and automation drives the adoption of AutoML tools and platforms. Organizations seek AutoML solutions to streamline their operations, enhance decision making, and gain a competitive edge. By automating the model development and training process, AutoML accelerates the deployment of AI applications, resulting in cost savings and improved efficiency. Furthermore, as more industries integrate AI into their workflows, the AutoML market is poised to experience sustained growth.

Data Explosion And The Need For Data-Driven Insights

Another driving force behind the growth of the AutoML market is the explosion of data generated by businesses and individuals. The digital era has ushered in an era of vast datasets, presenting both opportunities and challenges. Organizations are inundated with data from various sources, including IoT devices, social media, and sensors. Extracting valuable insights from this deluge of information is paramount for decision-making and gaining a competitive edge. AutoML plays a pivotal role in managing and making sense of this data. It automates the process of model selection, feature engineering, and hyperparameter tuning, allowing organizations to harness the power of their data more efficiently. As businesses recognize the importance of data-driven decision-making, they increasingly turn to AutoML to derive actionable insights from their data, driving the market's growth.

Shortage Of Data Science And Ml Expertise

The shortage of skilled data scientists and machine learning experts is a critical factor driving the adoption of AutoML. Building and deploying machine learning models traditionally required a high level of expertise in data science, programming, and statistics. However, the demand for such experts has outpaced supply, resulting in a talent gap. AutoML addresses this challenge by enabling individuals with varying levels of technical expertise to engage in machine learning tasks. It automates complex aspects of model development, making it accessible to business analysts, domain experts, and other professionals who may lack specialized machine learning skills. This democratization of machine learning empowers organizations to harness the potential of AI without relying solely on a limited pool of experts, driving the expansion of the AutoML market.

MARKET OPPORTUNITIES

Democratization of Machine Learning

One of the most significant opportunities for the Global Automated Machine Learning (AutoML) Market lies in the democratization of machine learning. Historically, machine learning was a specialized field, accessible only to a limited pool of data scientists and machine learning experts. However, AutoML tools and platforms have transformed this landscape by making machine learning more accessible to a broader range of users and use cases. AutoML empowers individuals with varying levels of technical expertise to participate in machine learning tasks. This democratization opens up new possibilities for organizations to tap into the potential of AI. Business analysts, domain experts, and even non-technical users can now utilize AutoML tools to develop predictive models, analyze data, and gain valuable insights. This broader adoption of machine learning drives the growth of the AutoML market, as more industries and functions integrate AI into their operations, leading to new opportunities for innovation and growth.

Expansion of AI Use Cases

The ever-expanding landscape of AI use cases presents another significant opportunity for the AutoML market. As organizations continue to explore and implement AI-driven solutions, new applications and industries emerge as potential markets for AutoML. AutoML tools can adapt to a wide array of use cases, from predictive analytics in healthcare to demand forecasting in retail and anomaly detection in cybersecurity. The ability of AutoML to quickly build and deploy predictive models enables organizations to address specific business challenges effectively. For instance, AutoML models can predict customer behavior, optimize pricing strategies, identify manufacturing defects, and even automate content recommendation systems. As industries discover the potential of AI in solving complex problems, the AutoML market can capitalize on this trend by providing the tools and platforms necessary to bring AI solutions to various sectors. This expansion of AI use cases not only diversifies the AutoML market but also creates new opportunities for growth and innovation in the field.

MARKET RESTRAINTS

Lack Of Awareness And Education

The Global AutoML Market is the lack of awareness and education about AutoML and its capabilities. Many businesses and decision-makers remain uninformed about the potential benefits of AutoML, hindering its adoption. A substantial portion of organizations may not fully grasp how AutoML can streamline their operations, reduce costs, and enhance performance. This lack of awareness creates a barrier to entry, preventing AutoML from reaching its full market potential. Moreover, the absence of education and training on AutoML may deter organizations from investing in the technology. Decision-makers may be reluctant to adopt a solution they do not fully understand, leading to missed opportunities for efficiency gains and competitive advantages. Addressing this restraint requires concerted efforts in raising awareness, providing educational resources, and demonstrating the tangible benefits of AutoML to decision-makers across industries.

Skilled Talent Shortage

The significant restraint affecting the AutoML market is the shortage of skilled data scientists and machine learning experts. While AutoML aims to democratize machine learning and make it accessible to a broader audience, there is still a need for experts who can effectively implement, customize, and maintain AutoML solutions. However, the demand for these skilled professionals far exceeds the available talent pool. This shortage of expertise can slow down the adoption of AutoML, as organizations struggle to find the right talent to develop and deploy machine learning models. The competition for qualified data scientists and machine learning engineers intensifies, lengthening the time required to fill positions and increasing labor costs. Additionally, as AutoML platforms continue to evolve, professionals need ongoing training and upskilling to keep pace with the latest advancements. The shortage of skilled talent extends beyond data scientists and encompasses areas such as data management and cloud computing, further complicating the successful implementation and adoption of AutoML solutions. To address this restraint, organizations need to invest in training and development programs and foster a culture of continuous learning in the field of machine learning.

Major Classifications are as follows:

By Offering

  • Solutions
  • Services
  • Consulting Services
  • Deployment & Integration
  • Training, Support, and Maintenance

By Deployment Mode

  • Cloud-Based
  • On-premises

By Application

  • Data Processing
  • Feature Engineering
  • Model Selection
  • Hyperparameter Optimization & Tuning
  • Model Ensembling
  • Other

By Vertical

  • BFSI
  • Retail & eCommerce
  • Healthcare & life sciences
  • IT & ITeS
  • Manufacturing
  • Automotive, Transportations, and Logistics
  • Others

By Region

  • North America
    • US
    • Canada
  • Latin America
    • Brazil
    • Mexico
    • Argentina
    • Rest of Latin America
  • Europe
    • UK
    • Germany
    • France
    • Italy
    • Spain
    • Russia
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Rest of Asia Pacific
  • Rest of the World
    • Middle East
      • UAE
      • Saudi Arabia
      • Israel
      • Rest of the Middle East
    • Africa
      • South Africa
      • Rest of the Middle East & Africa

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.

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Furthermore, our methodology incorporates technology-based research techniques, such as data mining, text analytics, and predictive modelling, to uncover hidden patterns, correlations, and trends within the data. This data-driven approach enhances the accuracy and reliability of our analysis, enabling us to make informed and actionable recommendations.

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