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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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.