Automated Machine Learning Market worth $13.88 billion by 2030, growing at a CAGR of 35.70% - Exclusive Report by 360iResearch

The "Automated Machine Learning Market by Automation Type (Data Processing, Feature Engineering, Modeling), Deployment (Cloud, On-premises), Application - Global Forecast 2024-2030" report has been added to 360iResearch.com's offering.

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Automated Machine Learning (AutoML) automates the end-to-end process of applying machine learning (ML) to real-world problems, comprising data preprocessing, model selection, hyperparameter tuning, and model evaluation. This automation aims to make ML more accessible, reducing the need for extensive domain expertise and streamlining processes to save time and resources. AutoML addresses the skill gap caused by the shortage of skilled data scientists, offers efficiency by automating tedious ML tasks, and ensures consistent performance across various datasets by minimizing human error and bias. Its applications span across multiple industries, including healthcare for predictive analytics, finance for fraud detection and risk modeling, retail for demand forecasting and recommendation systems, manufacturing for predictive maintenance and supply chain optimization, and marketing for customer churn prediction and targeted advertising. The end-use scope encompasses enterprise IT departments, data science service providers, healthcare providers, financial institutions, retail chains, and research institutions. Key factors driving market growth include increased data availability, advancements in ML algorithms, cost-effectiveness by reducing the need for specialized data scientists, and significant time savings. Potential opportunities in the AutoML market include integration with cloud services, development of edge computing models, and industry-specific solutions such as those for autonomous vehicles and IoT. Recommendations for capturing these opportunities include forming partnerships with industry-specific vendors, investing in intuitive and user-friendly interfaces, and ensuring regulatory compliance. However, challenges such as data quality issues, complexity in interpretability, scalability concerns, and security issues can hinder progress. Areas ripe for innovation include explainable AI (XAI), hybrid models combining human expertise with AutoML, and meta-learning to enhance the learning speed from previous tasks and datasets. The AutoML market remains dynamic and rapidly growing, characterized by ongoing innovation and a high potential for disruption across several industries.

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Market Segmentation & Coverage:

This research report categorizes the Automated Machine Learning Market in order to forecast the revenues and analyze trends in each of following sub-markets:

Based on Automation Type, market is studied across Data Processing, Feature Engineering, Modeling, and Visualization.

Based on Deployment, market is studied across Cloud and On-premises.

Based on Application, market is studied across Automotive, Transportations, and Logistics, Banking, Financial Services, and Insurance, Government & Defense, Healthcare & Life Sciences, It & Telecommunications, and Media & Entertainment.

Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

Key Company Profiles:

The report delves into recent significant developments in the Automated Machine Learning Market, highlighting leading vendors and their innovative profiles. These include Aible, Inc., Akkio Inc., Altair Engineering Inc., Alteryx, Amazon Web Services, Inc., Automated Machine Learning Ltd., BigML, Inc., Databricks, Inc., Dataiku, DataRobot, Inc., Google LLC by Alphabet Inc., H2O.ai, Inc., Hewlett Packard Enterprise Company, InData Labs Group Limited, Intel Corporation, International Business Machines Corporation, Microsoft Corporation, Oracle Corporation, QlikTech International AB, Runai Labs Ltd., Salesforce, Inc., SAS Institute Inc., ServiceNow, Inc., SparkCognition, Inc., STMicroelectronics, Tata Consultancy Services Limited, TAZI AI, Tellius, Inc., Weidmuller Limited, Wolfram, and Yellow.ai.

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Key Topics Covered:

1. Preface
2. Research Methodology
3. Executive Summary
4. Market Overview
5. Market Insights
6. Automated Machine Learning Market, by Automation Type
7. Automated Machine Learning Market, by Deployment
8. Automated Machine Learning Market, by Application
9. Americas Automated Machine Learning Market
10. Asia-Pacific Automated Machine Learning Market
11. Europe, Middle East & Africa Automated Machine Learning Market
12. Competitive Landscape
13. Competitive Portfolio

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