The Business Research Company’s Automated Machine Learning (AutoML) Global Market Report 2023 is the most detailed report available on the market, including broad forecast periods and multiple geographies. The report covers the historic period – 2010-2021, and the forecast period – 2023-2032. The Automated Machine Learning (AutoML) Global Market Report 2023 evaluates automated machine learning (automl) market size, growth rate, drivers, trends, and major companies.
The report provides a global perspective by covering 60 geographies and focusing on major economies in each region – Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
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The burgeoning global landscape of automated machine learning (AutoML) is poised for remarkable growth, projected to ascend from $0.77 billion in 2022 to a substantial $1.15 billion in 2023, demonstrating an impressive compound annual growth rate (CAGR) of 45%. However, this surge is set against the backdrop of global economic turmoil catalyzed by the Russia-Ukraine conflict, which has disrupted the ongoing efforts to recover from the seismic shockwaves of the COVID-19 pandemic.
The repercussions of this geopolitical strife reverberate across nations, manifesting in the form of stringent economic sanctions, soaring commodity prices, and perturbations within the global supply chains. This multifaceted crisis has precipitated inflationary pressures across both goods and services, further exacerbated by the cascading effects of increased interest rates. Against this complex backdrop, the AutoML market is poised to continue its ascent, anticipated to reach a substantial $5.03 billion in 2027, maintaining a CAGR of 44.6%. This trajectory underscores the resilience and potential of the AutoML sector in an ever-evolving global economy.
The burgeoning demand for sophisticated fraud detection solutions is set to serve as a robust catalyst for the progressive expansion of the automated machine learning (AutoML) market. The critical domain of fraud detection entails the intricate process of identifying and thwarting fraudulent activities or illicit behaviors within the realms of a system or organization. Herein, the role of automated machine learning (AutoML) shines prominently, equipped with its formidable capacity to ingest, process, and meticulously scrutinize vast troves of data. It adeptly discerns intricate patterns and anomalies that may serve as red flags, indicative of potential fraudulent activities.
An illustrative example, hailing from February 2023, casts a vivid light on this burgeoning landscape. The Financial Crimes Enforcement Network (FinCEN), a prominent U.S. government agency specializing in the collection and analytical scrutiny of financial transaction data, reported a momentous surge. In 2021, the country’s banking institutions transmitted an impressive 350,000 suspicious activity reports (SARs) to FinCEN, signaling a substantial 23% upswing in comparison to the preceding year. This heightened trajectory persisted through 2022, with an astonishing tally of over 680,000 SARs, nearly doubling the prior year’s figures. Thus, the escalating demand for advanced fraud detection solutions exerts a pronounced influence on the accelerated advancement of the automated machine learning (AutoML) market, underscoring its pivotal role in contemporary security endeavors.
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The automated machine learning (automl) market segments in the report are:
1) By Offering: Solutions, Services
2) By Deployment: Cloud, On-Premises
3) By Enterprise: Small And Medium Enterprise, Large Enterprise
4) By Application: Data Processing, Feature Engineering, Model Selection, Hyperparameter Optimization And Tuning, Model Assembling, Other Applications
5) By End User: Banking, Financial Services And Insurance (BFSI), Retail And E-Commerce, Healthcare, Manufacturing, Other End Users
The table of contents in TBRC’s automated machine learning (automl) market report includes:
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Top 5 Major Key Players Are:
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