The automated machine learning market provides AI-powered data analysis and model building without extensive coding. Automated ML platforms automate labor-intensive data preparation, model training and tuning and model deployment tasks using techniques like neural architecture search and federated learning. This allows researchers, data scientists and developers lacking machine learning expertise to leverage ML models.
The global automated machine learning market is estimated to be valued at USD 3.13 billion in 2024 and is expected to exhibit a CAGR of 48.2% over the forecast period 2024-2031. Key Takeaways Key players operating in the automated machine learning market are Google Cloud, Anthropic, H2O.ai, IBM, Microsoft, SAS, Domino Data Lab, DataRobot, BigML and SAP. Key players are focusing on product development and expansion strategies to gain a competitive edge in this high growth market. Automated Machine Learning Demand models across industries without extensive coding skills is driving the adoption of automated machine learning platforms. Automated ML solutions help organizations leverage advanced analytics without requiring data science expertise inhouse. The need to extract insights from huge volumes of complex data is further fueling demand. North America currently dominates the global automated machine learning market. However, Asia Pacific is emerging as a major market with China, Japan and India expected to increasing adoption. Growing technology investments to gain competitive advantages through data-driven decision making will support market growth. Market key trends: One of the key trends driving growth in the automated ML market is edge computing. As more IoT devices generate data at the network edges, deploying machine learning models closer to the data source is critical to enable real-time analytics and insights. Edge-capable automated ML platforms that build, deploy and manage ML workflows from core data centers to edge devices will see rising demand. This allows capturing valuable insights from streaming IoT data with added capabilities like privacy and lower latency.
Porter’s Analysis Threat of new entrants: High initial investments required for research and development act as a barrier for new entrants in this market. however, low brand Loyalty can encourage new players to enter the market. Bargaining power of buyers: Buyers have moderate to high bargaining power in this market due to availability of alternatives and price sensitivity of customers.Buyers can negotiate on price depending on the business size. Bargaining power of suppliers: Suppliers have moderate bargaining power due to differentiated product offerings and switching costs for buyers. Suppliers compete based on product quality, relationship and switching costs. Threat of new substitutes: Threat of substitution is medium as industry will look towards artificial intelligence for alternatives. However, switching costs for established products pose a challenge to substitutes. Competitive rivalry: The market is competitive with presence of global and local players. Players compete based on product differentiation, pricing and technological innovations. Geographical regions: North America dominates the automated machine learning market currently in terms of value, mainly due to growing adoption of AI technologies across major industries in the US and high investments in AI startups by major tech companies. Asia Pacific is expected to be the fastest growing regional market for automated machine learning. This is attributed to rapid digitalization, improving data processing capabilities,rising overseas investments in AI and increasing focus of governments to leverage AI for economic and infrastructure development in emerging countries such as India and China.
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