AUTOMATED MACHINE LEARNING MARKET SIZE AND SHARE ANALYSIS - GROWTH TRENDS AND FORECASTS (2024 - 2031)

Automated Machine Learning Market Size and Trends

Global automated machine learning market is estimated to be valued at USD 3.13 Bn in 2024 and is expected to reach USD 49.23 Bn by 2031, growing at a compound annual growth rate (CAGR) of 48.2% from 2024 to 2031.

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The need for automated processes to handle increasing data volumes and the lack of machine learning talent are driving increased adoption of automated machine learning tools and platforms by organizations across industries. With increasing demand for embedded machine learning across industries, the market for customized automated ML solutions and services has potential for strong expansion in the coming years. The study also finds that alliances help strengthen product development and allow players to tap new customer segments together.

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Insights, By Application, Automation of Laborious Tasks Drives Data Processing Dominance

By Application, Data Processing segment is expected to contribute the highest share of 39.2% in 2024 owing to the automation it provides for tedious data cleaning and formatting tasks. As machine learning models require large volumes of high-quality structured data to learn from, the data preprocessing stage is notoriously labor-intensive as it involves activities like data sourcing, cleaning, merging, filtering and encoding.

Insights, By Offering, Demand for packaged solutions drives highest share of Solution offerings

By offering, the solution segment is expected to contribute the highest share of 54.2% in 2024 owing to the convenience and standardization it provides organizations. While consulting services enable custom development of automated machine learning workflows, solutions offer packaged applications that can be deployed straight out of the box. This plug-and-play functionality addresses a key barrier to AI adoption as it eliminates the need for in-house AI expertise and specialized resource requirements.

Insights, By Vertical, Expanding Data-driven Businesses Drive Banking Adoption

By Vertical, the BFSI segment is expected to contribute the highest share of 38.3% in 2024 due to the data-intensive and dynamic nature of banking, financial services and insurance businesses. With growing digitization across channels, BFSI operators are accumulating vast volumes of customer and transactional data from both traditional and emerging digital touchpoints. At the same time, customer preferences and risk profiles are also evolving rapidly with changing economic conditions, regulations and competitive forces.

Regional Insights

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Dominating Region: North America

North America is the leading region and is expected to account for 41.2% market share in 2024. This growth is attributed to strong technology adoption, sizable investments in AI and machine learning by key players, and supportive government policies promoting innovation. The region is home to major tech giants that are at the forefront of developing automated machine learning capabilities.

Fastest-Growing Region: Asia Pacific

The Asia Pacific region is expected to account for 34.3% market share in 2024, driven by extensive tech startup ecosystem in countries like India and China, increasing digitization across industries, and government initiatives to develop homegrown AI technologies. Several local companies are emerging as important contributors with competitive offerings.

Automated Machine Learning Market Outlook for Key Countries

Significant Investments and Innovative Solutions

The US automated machine learning market continues to be fueled by heavy investments from corporate and venture capital funding into new technologies. Companies like Google and Microsoft have introduced many innovative solutions. The trend towards user-friendly automated machine learning is having a notable impact on the structure of the US market. While specialized AI vendors still lead for highly complex enterprise needs, the proliferation of easy-to-use tools is lowering the barrier of entry and expanding the potential customer base beyond large corporations.

Rise in Automation Capabilities in China

China's market is scaling rapidly as local AI champions ramp up automated ML capabilities for applications across various sectors important for the country's development priorities. Players like Alibaba and Baidu are at the forefront of these efforts. Major technology hubs in countries like Beijing, Shanghai, Shenzhen and Hangzhou have seen a proliferation of startups developing automated machine learning tools tailored for specific domains and use cases.

Favorable Market Conditions in India to Boost Startup Ecosystem

India continues to lead with its technical talent pool and collaborative AI research environment. Startups like Anthropic are leveraging these strengths to build competitive products. The automated machine learning market in India has seen significant growth and transformation over the past few years. As machine learning and AI technologies become more widely adopted across various industries, there is a rising demand for tools and platforms that make machine learning more accessible for everyone.

Rise in Aging Population to Create Opportunities for Automated Solutions

Japanese companies are strengthening automated machine learning solutions to boost productivity amid the country's aging population. Fujitsu recently unveiled an enterprise platform targeting manufacturing. The rising adoption of automated machine learning tools and technologies by businesses in Japan is having a profound impact on the nation's automated machine learning market. As companies recognize the value of using ML to gain insights from their vast amounts of data, there is growing demand for automated solutions that allow non-experts and general business users to develop ML models with little coding knowledge.

Market Concentration and Competitive Landscape

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Top Strategies Followed by Automated Machine Learning Market Players

• Established Players: Established players in the automated machine learning market focus heavily on research and development to introduce innovative products. Industry leaders such as Google, Microsoft and IBM invest over 10% of their annual revenues into R&D. This focuses on developing high-performance machine learning algorithms and solutions. These companies also form strategic partnerships with other technology giants and hardware manufacturers.
• Mid-level Players: Mid-level automated machine learning providers offer affordable solutions to remain competitive. They price products 30-50% lower than top vendors by reducing overhead costs. Some adopt a SaaS-based business model without upfront software fees to lower entry barriers. These companies also partner with system integrators, consulting firms and technology startups. Such collaborations help augment capabilities, scale operations and gain additional customer reach.
• Small Players: Small players focus on specialized niches within the automated machine learning space. Some target specific industries with domain-specific algorithms and tools. Others provide consulting and implementation services for specialized predictive modeling needs. To compete against larger rivals, small vendors rapidly adopt new technologies. They also form local partnerships with technology incubators, IT service providers and universities. This helps address regional market needs and validates products with pilot customers.

Emerging Startups in the Automated Machine Learning Market

• Innovation: Several startups are developing innovative machine learning technologies. Anthropic builds general-purpose self-supervised AI models to solve complex tasks without human labeling. Anthropic's approach could help reduce dependence on labeled data and lower the costs of deploying advanced machine learning. Another startup, Valohai, offers a platform for managing the machine learning lifecycle including model development, version control, and monitoring. This type of platform solution could simplify and streamline ML operations at scale.
• Sustainability- Sustainability is a focus for some emerging startups. Cocrop envisions using computer vision and AI to estimate agricultural yields and help farmers use resources efficiently. This could contribute to reducing agriculture's environmental impact over time. Another startup, Circulor, applies blockchain to map ethical supply chains for minerals. Such traceability solutions may drive adoption of recycled materials in electronics and automotive industries.
• Niche Specialization: Some smaller players address specialized or niche markets. for instance, Onna develops tools specifically for machine learning in the fashion sector. Anthropic supports ML access in emerging languages like Bengali. Startups also partner with large players to help drive innovation - Anthropic partners with Microsoft to expand AI capabilities for visual recognition tasks. Such targeted offerings and partnerships allow startups to significantly contribute to the automated machi.

Automated Machine Learning Industry News

Automated Machine Learning Market Industry News:

• In September 2023, Fujitsu, in partnership with the Linux Foundation, released AutoML and AI fairness technologies as open-source software, aiming to improve accessibility and fairness in AI model deployment​.
• In March 2023, TDK Corporation's Qeexo unveiled an AutoML solution for Arm Keil MDK, aimed at enhancing embedded machine learning workflows.
• In February 2023, AWS introduced new features for Amazon SageMaker Autopilot, allowing users to select specific algorithms during model creation, which increased control over the model-building process.
• In May 2023, JADBio and DiamiR Biosciences formed a partnership to create predictive models for brain health diagnostics using AutoML technologies.
• In April 2022, DataBricks launched the DataBricks AutoML platform, which simplifies model development by automating pre-processing and model training​.

Key Takeaways from Analyst

• One of the key drivers for this growth is the increasing shortage of data science and machine learning experts. Automated machine learning tools allow organizations without dedicated data science teams to develop ML models. This expands the potential customer base for ML applications. Additionally, machine learning is becoming more central to business strategies across industries. Automation makes complex ML processes more accessible and speeds up experimentation and model development.
• However, concerns around reliability and control over model building may restrain the early adoption of fully automated tools. Many companies want to balance automation with human oversight in ML processes. Additionally, integrating automated systems with existing enterprise IT infrastructures can be challenging.
• North America currently dominates the automated ML space due to widespread early adoptions among technology companies. However, Asian markets, especially China, are expected to see faster growth. This is because of increasing government support for AI development and growing investments by technology giants based in the region. In addition, large volumes of data and low-cost trials provide Asian players advantages to develop automated solutions tailored for their markets.
• Overall, while fully-automated tools may not see universal adoption, having different levels of automation for different ML tasks will unlock new use cases.

Market Report Scope

Market Dynamics

Market Driver - Need for Data-driven Decision Making

With the rise of big data and easy availability of data from various sources, companies are placing growing emphasis on making strategic and operational decisions backed by thorough data analysis. However, traditional data analysis techniques requiring manual data preparation and complex model building and tuning are time-consuming and resource-intensive. Many mid-sized or smaller companies lack dedicated data science teams who can devote their time and skills to leverage machine learning and AI. This has fueled the demand for automated machine learning technologies that can help organizations tap into the power of data-driven decision making, without requiring deep expertise in complex algorithms or programming.

Market Challenge - Data Quality Issues Hampering Automated Machine Learning Outputs

The lack of good quality, clean data continues to pose challenges for the widespread adoption of automated machine learning. While automated tools have become quite advanced at developing models without human intervention, the outputs are only as good as the data that is fed into these systems. Issues around missing or incorrect values, inconsistent formats, and other data quality problems can negatively impact the ability of automated ML to identify patterns and relationships. This leads to models that do not perform as well as expected when deployed. Data preparation and cleaning still requires heavy human involvement. Addressing data quality at scale remains an obstacle especially for organizations working with legacy systems and databases not originally designed for modern machine learning applications.

Market Opportunity- Scope of Customizing Automated Machine Learning Workflows for Specific Domains

The ability to customize automated machine learning workflows for specific industry domains and use cases presents a major opportunity for growth. While general-purpose automated tools have succeeded in automating parts of the model development process, customization allows tapping into unique domain knowledge and constraints. Tailoring automated ML for targeted applications such as predictive maintenance, customer churn prediction, fraud detection, and more ensures models are developed with relevant features and parameters for the problem at hand. This level of customization appeals to enterprises with specialized modeling needs and also creates an ongoing professional services opportunity for vendors.

Market Segmentation

Market Segmentation

• By Application Insights (Revenue, USD Bn, 2019 - 2031)
  • Data Processing
  • Feature Engineering
  • Model Selection
  • Model Ensembling
  • Others
• By Offering Insights (Revenue, USD Bn, 2019 - 2031)
  • Solution
  • Services
• By Vertical Insights (Revenue, USD Bn, 2019 - 2031)
  • BFSI
  • Retail & E-commerce
  • Healthcare & Life Sciences
  • IT & Ites
  • Others
• Regional Insights (Revenue, USD Bn, 2019 - 2031)
  • North America
    • U.S.
    • Canada
  • Latin America
    • Brazil
    • Argentina
    • Mexico
    • Rest of Latin America
  • Europe
    • Germany
    • U.K.
    • Spain
    • France
    • Italy
    • Russia
    • Rest of Europe
  • Asia Pacific
    • China
    • India
    • Japan
    • Australia
    • South Korea
    • ASEAN
    • Rest of Asia Pacific
  • Middle East
    • GCC Countries
    • Israel
    • Rest of Middle East
  • Africa
    • South Africa
    • North Africa
    • Central Africa