The global AutoML market size will grow on the backdrop of 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 to boost customer trust and ensure compliance with laws.
Automated machine learning (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.
Bullish demand for AutoML is mainly attributed to its ability to help enterprises boost insights and enhance model accuracy by minimizing chances for error or bias. End-users, including BFSI, healthcare, IT & telecom and retail, are expected to inject funds into AutoML to rev up their AI efforts to create a valuable pipeline to automate data preprocessing, model selection and pre-trained models. Prominently, the healthcare sector exhibited increased traction for machine-learning-powered chatbots to leverage contactless screening and boost the patient experience. The use of Automated machine learning has become instrumental in boosting service quality, enhancing bed occupancy estimation, boosting patient billing estimation and minimizing costs.
Fraud Detection; Sales & Marketing Management; Medical Testing; Transport Optimization
BFSI; IT & Telecom; Healthcare; Government; Retail; Manufacturing; Others
North America; Europe; Asia Pacific; South America; MEA
Stakeholders anticipate the AutoML services to gain ground on the back of soaring demand for maintenance and consulting services. Organizations and enterprises are likely to further their investments in services to streamline workflows and boost productivity. The application of Automated machine learning will further grow due to an increased chance of reduced errors and human bias.
In terms of deployment type, the cloud-based segment will exhibit notable growth due to the trend for custom ML models and the demand for scalability. Cloud AutoML has become trendier across businesses for image recognition and training and managing models. Furthermore, some factors, such as faster turnaround time for the production-ready models, increased accuracy, and simple graphical user interface has encouraged organizations to invest in cloud Automated machine learning .
Based on application, the fraud detection segment will account for a considerable share of the AutoML market share. 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.
With respect to vertical, 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. AutoML 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.
North America AutoML market share will observe a prominent growth in the wake of rising investments from the BFSI, retail, and healthcare sectors. The U.S. and Canada will witness bullish investments in AI technology and machine learning to automate workflow and enable firms’ data scientists to prioritize more complex issues. While the media, Information and Communication Technology (ICT) sector and professional services will augment investments in machine learning and AI, manufacturing, mining, and utilities could be in the nascent stage of adoption of the technology across the region. The next few years are expected to provide compelling growth opportunities for AutoML companies as end-users seek a new wave of opportunities in automation technologies to take the lead in the industry.
The competitive scenario alludes to an increased focus on innovation, technological advancements, collaboration, product rollouts and mergers & acquisitions. To illustrate, in March 2022, Oracle announced the addition of AutoML to its MySQL HeatWave service. In June 2022, Google expanded its managed AI service Vertex, featuring Tabular Workflows to boost customizability.
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