Global Automated Machine Learning Market Outlook to 2028

Global Automated Machine Learning Market Overview

• The Global Automated Machine Learning (AutoML) Market was valued at USD 1 billion in 2023. This market is primarily driven by the increasing demand for automated solutions in data science, the rising need for advanced analytics and artificial intelligence (AI) capabilities, and the growing focus on reducing the complexity and cost associated with traditional machine learning (ML) processes.
• The major players in the global AutoML market include DataRobot, H2O.ai, Google LLC, Microsoft Corporation, and IBM Corporation. These companies are at the forefront of developing innovative AutoML platforms, focusing on enhancing usability, scalability, and integration with existing enterprise systems.
• DataRobot's AutoML 2.0 platform, launched in 2023, simplifies AI model deployment with 21% faster results using Quickrun Autopilot, No-Code AI Apps built directly from model leaderboards, and support for datasets up to 5GB for Auto Time Series modelling, enabling end-to-end automation for enterprises.
• In 2023, North America dominated the global AutoML market due to the high adoption of AI technologies, investments in research and development, and the presence of major technology companies.

Global Automated Machine Learning Market Segmentation

The global Automated Machine Learning market is segmented by type, application, and region.

• By Type: The market is segmented into cloud-based and on-premises solutions. In 2023, cloud-based solutions held the highest market share due to the increasing adoption of cloud computing and the scalability offered by cloud platforms.
• By Application: The market is segmented into finance, healthcare, retail, manufacturing, and others. In 2023, the finance segment held the largest market share, driven by the growing use of AI for risk management, fraud detection, and customer analytics.

• By Region: The global AutoML market is segmented into North America, Europe, Asia-Pacific (APAC), Middle East & Africa (MEA), and Latin America. In 2023, North America held the largest market share, supported by advanced technological infrastructure and the early adoption of AI technologies.

Global Automated Machine Learning Market Competitive Landscape

• H2O.ai: H2O.ai recently released H2O Wave, an open-source platform for building AI applications, and enhanced its Hybrid Cloud offering with improved user interfaces and the AI App Store. The company was also recognized as a leader in the Gartner Magic Quadrant for Data Science and Machine Learning Platforms in 2023.
• Google LLC: In April 2024, Google announced a major reorganization to streamline AI model development by merging its Google Brain team with DeepMind into a single entity called Google DeepMind. This consolidation aims to accelerate AI innovation.

Global Automated Machine Learning Market Analysis

Global Automated Machine Learning Market Growth Drivers:

• Increasing Demand for Automated Solutions: The rise of automated data science solutions is evident as organizations seek to streamline AI model deployment. Automating tasks like data preprocessing and model selection allows businesses to deploy AI models 30-50% faster, addressing the critical shortage of data science expertise and enhancing operational efficiency in various sectors.
• Growing Applications in AI and Analytics: AutoML is transforming industries like healthcare, automating complex ML tasks for disease diagnosis, treatment optimization, and drug discovery. In finance, automated solutions have reduced fraud detection time by up to 40%, while enhancing diagnostic accuracy in healthcare, showcasing its versatility and effectiveness.
• Reduction in Complexity and Cost: By automating repetitive tasks, AutoML reduces the complexity and time required to build and deploy machine learning models, lowering barriers to AI adoption by around 60%. Cloud-based AutoML solutions offer flexibility and cost-effectiveness, making AI accessible for SMEs without extensive resources.

Global Automated Machine Learning Market Challenges:

• Data Privacy and Security Concerns: The use of cloud-based AutoML platforms raises data privacy and security concerns, particularly in sensitive sectors like healthcare. Around 70% of healthcare organizations express reluctance to adopt cloud solutions due to fears surrounding patient data privacy, prompting a preference for on-premise frameworks that ensure compliance with strict regulations.
• Limited Awareness in Emerging Markets: Limited awareness of AutoML's benefits poses a challenge in emerging markets. Studies indicate that over 60% of organizations in these regions lack an understanding of automated machine learning applications, hindering their ability to leverage AutoML for competitive advantage and stalling market growth and innovation.

Global Automated Machine Learning Market Government Initiatives:

• EU AI Act: The EU AI Act, the worlds first comprehensive AI regulation, aims to ensure the safe and ethical use of AI technologies. It categorizes AI systems by risk level, imposing strict rules on high-risk applications. The Act is expected to be fully operational by 2026, with penalties for non-compliance reaching up to 35 million or 7% of global turnover.
• US National AI Initiative: The US National AI Initiative promotes the development and adoption of AI technologies across various sectors to maintain global leadership in AI innovation. It emphasizes research funding, workforce development, and international collaboration, with an investment of $1.5 billion in AI research and development planned for 2024.

Global Automated Machine Learning Market Future Market Outlook

The Global Automated Machine Learning Market is expected to grow robustly over the coming years, driven by increasing demand for AI-powered automation, rising applications across various industries, and supportive government initiatives.

Global Automated Machine Learning Market Future Market Trends:

• Increased Use in Predictive Analytics: By 2028, the use of AutoML in predictive analytics is expected to rise, particularly in finance and healthcare. Research indicates that organizations leveraging AutoML for predictive analytics have improved decision-making efficiency, with companies like PayPal reporting a fraud detection model accuracy increase from 89% to 94.7% after adopting AutoML tools.
• Advancements in AI Model Explainability: Advancements in AI model explainability are anticipated by 2028, with AutoML platforms expected to enhance transparency and interpretability. Over 60% of data scientists believe improving model explainability is crucial for broader AI adoption, as organizations prioritize ethical AI practices and seek to build trust in automated decision-making systems.