Automated Machine Learning Solution Market – Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Offering (Platform and Service), By Deployment (On-Premise and Cloud), By Automation Type (Data Processing, Feature Engineering, Modeling, and Visualization), By Enterprise Size (Large Enterprises and SMEs), By End-Users (BFSI, Retail and E-Commerce, Healthcare, and Manufacturi
Published Date: November - 2024 | Publisher: MIR | No of Pages: 320 | Industry: ICT | Format: Report available in PDF / Excel Format
View Details Buy Now 2890 Download Sample Ask for Discount Request CustomizationAutomated Machine Learning Solution Market – Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Offering (Platform and Service), By Deployment (On-Premise and Cloud), By Automation Type (Data Processing, Feature Engineering, Modeling, and Visualization), By Enterprise Size (Large Enterprises and SMEs), By End-Users (BFSI, Retail and E-Commerce, Healthcare, and Manufacturi
| Forecast Period | 2024-2028 |
| Market Size (2022) | USD 1.12 Billion |
| CAGR (2023-2028) | 42.48% |
| Fastest Growing Segment | Manufacturing |
| Largest Market | North America |
Global automated machine learning solution market
Many areas of the industry now depend heavily on machine learning (ML). On the other hand, developing high-performance machine learning systems requires highly specialised data scientists and subject matter specialists. By enabling domain experts to automatically create machine learning applications without extensive statistical and machine learning skills, automated machine learning (AutoML) aims to reduce the need for data scientists.
Machine learning (ML) is being utilised more often in a variety of applications lately, but there aren't enough machine learning professionals to keep up with this increase. The goal of automated machine learning (AutoML) is to make machine learning more approachable. As a result, professionals should be able to install more machine learning systems, and using AutoML would need less skill than using ML directly. The technology's acceptance, nevertheless, is currently only moderate, which limits the
After the COVID-19 epidemic, organisations have been increasingly relying on intelligent solutions to automate their business operations, which is causing a rise in the use of AI. This pattern is anticipated to persist throughout the ensuing years, accelerating the adoption of AI in business operations.
Increasing Demand for Efficient Fraud Detection Solutions
Machine learning is used in a wide range of financial applications, including trading, process automation, credit scoring, and underwriting for loans and insurance. One of the major issues with financial security is financial fraud. Machine learning is currently being used for fraud detection applications to combat the rising danger of financial fraud.
Artificial Intelligence (AI) usage is increasing as businesses now turn to utilising next-generation technology. Businesses may employ artificial intelligence for a variety of purposes, including data collection and work process efficiency.
Slow Adoption of Automated Machine Learning Tools
Machine learning (ML) is being employed in a growing number of applications, but there aren't enough machine learning specialists to keep up with this expansion. The goal of automated machine learning (AutoML) is to make machine learning more approachable. As a result, specialists should be able to install more machine learning systems, and working with AutoML would need less skill than dealing with ML directly.
Growing Healthcare Applications
Many applications in the field of healthcare already make use of machine learning technology. This platform analyses millions of different data points from this sector vertical, forecasts results, and also offers rapid risk assessments and precise resource allocation.
The ability to diagnose and identify disorders and illnesses that might occasionally be challenging to recognise is one of this technology's most significant uses in healthcare. This can include a number of inherited conditions and tumours that are challenging to identify in the first stages. The IBM Watson Genomics is a notable illustration of this, demonstrating how genome-based tumour sequencing in conjunction with cognitive computing may facilitate cancer detection.
A major biopharmaceutical company called Berg, uses AI to provide medicinal treatments for diseases like cancer. All these factors are driving the market of
Resistance among Users Regarding Automated Machine Learning Solutions
The market's delayed adoption of automated machine learning solutions is mostly due to the limited uptake of machine learning technologies. Companies struggle to obtain the domain experts they need since there is a significant demand for them in the machine learning proper ability. Additionally, because it is expensive to hire these professionals, businesses are even less likely to adopt cutting-edge technology like machine learning.
Market Segmentation
The automated machine learning solution market is segmented into offering, deployment, automation type, enterprise size, end-users, company, and region. Based on offering, the market is segmented into platform and service
Market Players
Recent Developments
- Meta chose AWS as a significant and a long-termstrategic cloud supplier in December 2021. Together, Meta and AWS endeavouredto enhance PyTorch users' performance on AWS and quicken the process by whichprogrammers create, train, deploy, and use AI/ML models.
- In November 2021, SAS's flagship SAS Viyaplatform received support for open-source users. SAS Viya is used foropen-source utility and integration. The software user built an API-firststrategy that supported a machine learning-powered data preparation procedure.
- Dot Data, a supplier of full-cycle business AIautomation solutions, and Tableau, an analytics platform, announced acooperation in September 2021 to let Tableau users take advantage of dotData'sAI Automation Capabilities. Tableau users can perform full-cycle predictiveanalysis from raw data through data preparation and insight discovery throughAI-based predictions and actionable dashboards by combining Tableau's datapreparation and visualisation capabilities with dotData's enhanced insightsdiscovery and predictive modelling capabilities.
| Attribute | Details |
| Base Year | 2022 |
| Historic Data | 2018 – 2021 |
| Estimated Year | 2023 |
| Forecast Period | 2024 – 2028 |
| Quantitative Units | Revenue in USD Million and CAGR for 2018-2022 and 2024-2028 |
| Report Coverage | Revenue forecast, company share, growth factors, and trends |
| Segments Covered | Offering Deployment Automation Type Enterprise Size End-users Region |
| Regional Scope | North America; Asia-Pacific; Europe; South America; and Middle East & Africa |
| Country Scope | United States, Canada, Mexico, China, India, Japan, South Korea, Australia, Singapore, Malaysia, Germany, United Kingdom, France, Russia, Spain, Belgium, Italy, Brazil, Colombia, Argentina, Peru, Chile, Saudi Arabia, South Africa, UAE, Israel, and Turkey |
| Key Companies Profiled | Datarobot Inc., Amazon Web Services Inc., dotData Inc., IBM Corporation, Dataiku, EdgeVerve Systems Limited, Big Squid Inc., SAS Institute Inc., Microsoft Corporation, and Determined.ai Inc. |
| Customization Scope | 10% free report customization with purchase. Addition or alteration to country, regional & segment scope. |
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| Delivery Format | PDF and Excel through Email (We can also provide the editable version of the report in PPT/Word format on special request) |
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