Overview and Scope
Automated machine learning (AutoML) is the process of applying machine learning to practical issues AutoML automates machine learning models’ selection, composition, and parameterization, making the machine learning process more user-friendly and often providing faster, more accurate outputs than hand-coded algorithms.
Sizing and Forecast
The automated machine learning (automl) market size has grown exponentially in recent years. It will grow from $1.15 billion in 2023 to $1.67 billion in 2024 at a compound annual growth rate (CAGR) of 44.9%. The growth in the historic period can be attributed to complexity of machine learning, scarcity of data science talent, demand for speedy solutions, advancements in ai and computing power, cost efficiency.
The automated machine learning (automl) market size is expected to see exponential growth in the next few years. It will grow to $7.35 billion in 2028 at a compound annual growth rate (CAGR) of 44.9%. The growth in the forecast period can be attributed to ai integration across industries, expansion of iot and big data, rise of edge computing, hybrid cloud and on-premises solutions, regulatory compliance requirements. Major trends in the forecast period include automated feature engineering, federated learning advancements, explainable ai and model interpretability, automl for unstructured data, automl for autonomous systems.
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Segmentation & Regional Insights
The automated machine learning (automl) market covered in this report is segmented –
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
North America was the largest region in the automated machine learning (AutoML) market in 2023. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the automated machine learning (automl) market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa
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Major Driver Impacting Market Growth
The rising need for advanced fraud detection solutions is expected to propel the growth of the automated machine learning (AutoML) market going forward. Fraud detection involves identifying and preventing fraudulent activities or behaviors within a system or organization. Automated machine learning (AutoML) can be used for fraud detection by leveraging its capabilities to process and analyze large volumes of data, identify patterns, and detect anomalies that may indicate fraudulent activities. For instance, in February 2023, according to the Financial Crimes Enforcement Network (FinCEN), a US-based government agency that collects and analyzes information about financial transactions, in 2021, banking institutions sent over 350,000 suspicious activity reports (SARs) to FinCEN to identify suspected check fraud, a 23% increase over the number of SARs filed in 2020. This rising trend continued into 2022, with over 680,000, nearly double the previous year’s total. Therefore, the rising need for advanced fraud detection solutions drives the growth of the automated machine learning (AutoML) market.
Key Industry Players
Major companies operating in the automated machine learning (automl) market report are Google LLC, Microsoft Corporation, Amazon Web Services Inc., International Business Machines Corporation, Oracle Corporation, Salesforce Inc., Teradata Corporation, Alteryx, Altair Engineering Inc., EdgeVerve Systems Limited, TIBCO Software Inc., DataRobot Inc., Dataiku, BigPanda., H2O.ai Inc., KNIME, Cognitivescale, Anyscale Inc., RapidMiner, Squark AI Inc., Auger.AI, DotData Inc., BigML Inc., Valohai, DarwinAI, Aible Inc., SigOpt, Zerion, Xpanse AI, Neptune Labs
The automated machine learning (automl) market report table of contents includes:
1. Executive Summary
2. Automated Machine Learning (AutoML) Market Characteristics
3. Automated Machine Learning (AutoML) Market Trends And Strategies
4. Automated Machine Learning (AutoML) Market – Macro Economic Scenario
5. Global Automated Machine Learning (AutoML) Market Size and Growth
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32. Global Automated Machine Learning (AutoML) Market Competitive Benchmarking
33. Global Automated Machine Learning (AutoML) Market Competitive Dashboard
34. Key Mergers And Acquisitions In The Automated Machine Learning (AutoML) Market
35. Automated Machine Learning (AutoML) Market Future Outlook and Potential Analysis
36. Appendix
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