The Automated Machine Learning Market provides solutions that allow users without expertise in machine learning and data science to build, evaluate, and deploy models without expertise in coding or data science. Automated machine learning solutions automate various data science tasks such as data pre-processing, feature engineering, model training, evaluation, and deployment. The solutions are capable of ingesting large volumes of data from a variety of sources and selecting the best models to solve specific problems with minimal human intervention. These solutions help organizations deploy AI capabilities at scale without requiring advanced data science expertise.
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.
Automated Machine Learning Demand is increasing across industries as organizations are generating large volumes of complex data from various sources at an unprecedented rate. Automated machine learning helps harness this data through easy-to-use solutions for various use cases such as predictive maintenance, fraud detection, recommendation engines, personalized marketing, and remote monitoring.
Key Takeaways
Key players operating in the automated machine learning market are H2O.ai, Anthropic, International Business Machines Corporation, Microsoft Corporation, Amazon Web Services, Google, NVIDIA Corporation, DataRobot, SAS Institute, and SAP.
Key opportunities in the automated machine learning market include the growing adoption of AI across industry verticals, increasing investments from large technology companies and venture capital firms, and rising need for retraining and updating machine learning models continuously as data evolves.
Leading automated machine learning solution providers are targeting global expansion opportunities through partnerships, acquisitions, and establishing offshore development centers. For instance, DataRobot expanded its operations to Tokyo, Japan in 2020. Several startups in the market have also raised funding rounds to fast track their international business growth.
Market Drivers
The increasing need for deploying AI capabilities at large scale across organizations is a major market driver. Automated machine learning solutions help deploy AI with minimal effort and resources allowing organizations to derive value from data at scale.
Market Restraints
Limited capabilities of automated machine learning solutions in handling complex problems requiring advanced data science approaches act as a restraint for the market. In some critical use cases, human intervention is still required for accurate modeling.Data and model governance challenges also restrict the adoption of automated solutions in regulated industries.
Segment Analysis
The Automated Machine Learning market can be segmented into Cloud-based and On-premises sub-segments. The Cloud-based sub-segment accounts for the major market share and is expected to remain dominant during the forecast period. This is due to advantages like cost-effectiveness, ease of deployment, and scalability offered by cloud-based Automated Machine Learning solutions. Cloud services provide Machine Learning solutions without high upfront costs and infrastructure investments, thus attracting more end-users.
Global Analysis:
North America is currently the largest as well as the fastest growing regional market for Automated Machine Learning. High adoption of advanced technologies, strong technical expertise, and large presence of key Automated Machine Learning vendors drive the market growth in North America. Asia-Pacific is expected to grow at the highest CAGR during the forecast period owing to increasing digitization, big data analytics adoption, and rising investments by enterprises in advanced AI technologies across industries in countries like China and India.
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