PUBLISHER: Grand View Research | PRODUCT CODE: 1530044
PUBLISHER: Grand View Research | PRODUCT CODE: 1530044
The global automated machine learning market size is expected to reach USD 21,969.7 million by 2030, and growing at a CAGR of 42.2% from 2024 to 2030, according to a new report by Grand View Research, Inc. The global market size expanding on the backdrop of the rising need for advanced fraud detection solutions. Data analysis techniques, including supervised neural networks, have become highly sought-after to detect fraud through forecasting, clustering, and classification.
Organizations are expected to invest in automated machine learning (AutoML) to boost customer trust and ensure compliance with laws. AutoML is an innate process of automating iterative and time-consuming tasks. It enables developers, analysts, and data scientists to build ML models with productivity, efficiency, and high scale. AutoML has gained traction to minimize the knowledge-based resources needed to implement and train machine learning models.
The cloud-based segment will exhibit notable growth due to the trend for custom ML models and the demand for scalability. Cloud-based AutoML has become trendier across businesses for image recognition, training, and managing models. Furthermore, some factors, such as faster turnaround time for the production-ready models, increased accuracy, and simple graphical user interface have encouraged organizations to invest in cloud automated machine learning.
Moreover, the fraud detection is significantly augmenting the market growth. The trend is mainly due to real-time monitoring of suspicious activity. A palpable rise to do away with the unauthorized use of financial services will further the need for AutoML solutions and services. An uptick in online credit card fraud and a soaring number of transactions through wallets and cell phones will further expedite the demand for AutoML tools for fraud detection.
Additionally, the healthcare sector will emphasize the expansion of AutoML solutions following the latter's use in projecting disease progression, treatment planning, clinical information extraction, and patient care. Automated machine learning services could expand the application of ML algorithms in diabetes diagnosis and electronic health records (EHR), and Alzheimer's diagnosis analysis. To illustrate, in December 2020, Google rolled out AutoML Entity Extraction for Healthcare and healthcare Natural Language API to help healthcare professionals assess and review medical documents in a scalable and repeatable way.