Automated Machine Learning Automl Market
Automated Machine Learning (AutoML) Market Forecasts to 2028 - Global Analysis By Offering (Platform and Service), Deployment Type (Cloud and On-Premises), Automation Type, Enterprise Size, Application, End User and By Geography
|
Years Covered |
2020-2028 |
|
Estimated Year Value (2022) |
US $0.82 BN |
|
Projected Year Value (2028) |
US $7.58 BN |
|
CAGR (2022 - 2028) |
44.8% |
|
Regions Covered |
North America, Europe, Asia Pacific, South America, and Middle East & Africa |
|
Countries Covered |
US, Canada, Mexico, Germany, UK, Italy, France, Spain, Japan, China, India, Australia, New Zealand, South Korea, Rest of Asia Pacific, South America, Argentina, Brazil, Chile, Middle East & Africa, Saudi Arabia, UAE, Qatar, and South Africa |
|
Largest Market |
North America |
|
Highest Growing Market |
Asia Pacific |
According to Stratistics MRC, the Global Automated Machine Learning (AutoML) Market is accounted for $0.82 billion in 2022 and is expected to reach $7.58 billion by 2028 growing at a CAGR of 44.8% during the forecast period. Automated machine learning (AutoML) is a process that automates the more complex or basic steps of the machine-learning lifecycle. This makes it easier for people to engage in the development of AI without having a theoretical background or any prior expertise with machine learning. It benefits both the beginners and advanced AI practitioners. Users may upload data to training algorithms and have the system automatically choose the appropriate neural network design for a particular problem. Efficiency, scalability, and the elimination of recurring mistakes are all facilitated via AutoML.
According to a survey by O’Reilly found that only 20% of respondents reported using automated machine learning tools, while 48% had never heard of the technology.
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Market Dynamics:
Driver:
Growing user-friendly machine learning software
Expert-level machine learning knowledge is in high demand, yet there is a shortage. This may be seen in the fact that there are considerably more competent applicants than there are vacant positions. AutoML intends to close this gap by automating procedures that would otherwise be beyond the capabilities of anybody except a subject-matter expert. Anyone with basic technical expertise and learners may use AutoML as it is a user-friendly machine learning program with straightforward interfaces.
Restraint:
Shortage of skilled expertise
AutoML platforms demand users with solid backgrounds in programming, data science, and machine learning. Finding the necessary skills to create, implement, and manage AutoML models is a challenge for businesses. People that use AutoML platforms must always improve their skills and stay aware of the most recent developments in the industry. Due to a lack of qualified candidates, businesses are in severe rivalry with their competitors. The expansion of the market is being hampered by the skill scarcity in the fields of data management, data visualization, and cloud computing.
Opportunity:
Raising operational efficiency & cost savings
Machine learning is becoming more widely available, which resulted in huge cost reductions for enterprises. Businesses may save the expenses of investing in expensive infrastructure and employing specialist people by adopting AutoML solutions. Additionally, quicker AI solution development and implementation is boosting operational effectiveness and enhancing decision-making. Businesses may extend their offers and tap into new markets owing to the democratization of machine learning, which boosts profits and market share.
Threat:
Lack of awareness on AutoML
Many businesses are hesitant to implement AutoML despite its numerous benefits, including improved accuracy, scalability, and efficiency. The advantages of AutoML and the potential effects it might have on businesses may not be well-known to many corporate executives and decision-makers. The poor uptake of automated machine learning (AutoML) solutions is a major barrier to the market's expansion.
Covid-19 Impact
Organizations have relied more on intelligent solutions to automate their corporate processes during the COVID-19 outbreak. It is used in the methods for identifying COVID-19 instances. It has excelled in the areas of viral drug development as well as diagnostics, prognosis assessment, and epidemic forecasting. Numerous machine learning (ML) models that estimate the likelihood that a patient will survive a COVID-19 infection have been developed and compared using automated machine learning (autoML), and the top model has been determined. It evolved into a helpful tool for physicians to stratify patients in hospitals.
The data processing segment is expected to be the largest during the forecast period
The data processing segment is estimated to have a lucrative growth. The process of finding and fixing data problems may be automated with autoML. This involves finding missing numbers, fixing formatting issues with the data, and eliminating outliers. It involves methods that can be automatically applied to the data, such as normalization and standardization. By transforming the data into a more suitable format, the likelihood of mistakes and inconsistencies is decreased. It takes less time and effort to process data manually.
The cloud segment is expected to have the highest CAGR during the forecast period
The cloud segment is anticipated to witness the fastest CAGR growth during the forecast period, due to increasing internet connections. AutoML systems that are cloud-based provide more scalability and flexibility. When the workload or amount of data varies, they can simply scaled up or down as necessary. They often provide a pay-as-you-go pricing structure, which can be more economical for businesses with fluctuating workloads. It offers complete capability at a fair price with no initial outlay of funds.
Region with largest share:
North America is projected to hold the largest market share during the forecast period. It has significantly aided in the growth and development of the market for automated machine learning. US is one of the most developed countries in the region. In the US, the AutoML industry is expanding quickly, with several major businesses providing a range of products and services, from completely automated platforms to tools that help data scientists create machine learning models. In the US, usage of AutoML solutions has significantly increased, particularly in sectors like healthcare, banking, and retail.
Region with highest CAGR:
Asia Pacific is projected to have the highest CAGR over the forecast period, owing to its growing technological advancements. APAC countries are the most preferred destination for IT outsourcing. The rapid economic expansion, rising investments in IT infrastructure, growing uptake of innovative technologies, and expanding number of government efforts for the advancement of AI technologies may all be attributed to the market growth in this area.
Key players in the market
Some of the key players profiled in the Automated Machine Learning (AutoML) Market include Amazon Web Services Inc, DataRobot Inc., Qlik Technologies Inc, Microsoft Corporation, dotData Inc, Gnosis DA S.A., SAS Institute Inc, Google LLC, H2O.ai Inc, TAZI AI, RapidMiner, Squark, BigML Inc, Determined.ai Inc, Dataiku, IBM Corporation, EdgeVerve Systems Limited, Oracle and Enhencer LLC.
Key Developments:
In February 2023, IBM integrated StepZen's technology into its portfolio, with the aims to provide its clients with an end-to-end solution for building, connecting, and managing APIs and data sources, enabling them to innovate faster and generate more value from their data.
In November 2022, Amazon Web Services, Inc. has launched AWS Asia Pacific (Hyderabad) Region, its second such facility to augment services to customers in India. The jobs will be part of the AWS supply chain in India, including construction, facility maintenance, engineering, telecommunications and jobs within the country’s broader economy.
In November 2022, Microsoft announced that pre-orders for new Surface products, Surface Laptop 5 and Surface Pro 9, will commence in India via Amazon.in, Reliance Digital, Croma, Vijay Sales and select multi brand stores. The new Surface product launches bring the best of Microsoft together on a single device, enabling all users to participate, be seen, heard, and express their creativity.
In October 2022, Oracle partnered with NVIDIA, which enabled Oracle to offer its customers access to Nvidia's GPUs for use in machine learning workloads, enhancing the performance and capabilities of Oracle's machine learning tools.
In June 2022, Google LLC announced the expansion of its commitment in the United States with the creation of Google Public Sector, a new Google division that will focus on helping U.S. public sector institutions—including federal, state, and local governments, and educational institutions—accelerate their digital transformations.
Offerings Covered:
• Platform
• Service
Deployment Types Covered:
• Cloud
• On-Premises
Automation Types Covered:
• Feature Engineering
• Data Processing
• Hyperparameter Optimization & Tuning
• Model Ensembling
• Model Selection
• Visualization
Enterprise Sizes Covered:
• Small Enterprise
• Medium Enterprise
• Large Enterprise
Applications Covered:
• Fraud Detection
• Medical Testing
• Sales & Marketing Management
• Transport Optimization
• Other Applications
End Users Covered:
• BFSI
• Healthcare
• IT & Telecom
• Retail
• E-Commerce
• Manufacturing
• Government
• Other End Users
Regions Covered:
• North America
o US
o Canada
o Mexico
• Europe
o Germany
o UK
o Italy
o France
o Spain
o Rest of Europe
• Asia Pacific
o Japan
o China
o India
o Australia
o New Zealand
o South Korea
o Rest of Asia Pacific
• South America
o Argentina
o Brazil
o Chile
o Rest of South America
• Middle East & Africa
o Saudi Arabia
o UAE
o Qatar
o South Africa
o Rest of Middle East & Africa
What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2020, 2021, 2022, 2025, and 2028
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements
Free Customization Offerings:
All the customers of this report will be entitled to receive one of the following free customization options:
• Company Profiling
o Comprehensive profiling of additional market players (up to 3)
o SWOT Analysis of key players (up to 3)
• Regional Segmentation
o Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
o Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances
Table of Contents
1 Executive Summary
2 Preface
2.1 Abstract
2.2 Stake Holders
2.3 Research Scope
2.4 Research Methodology
2.4.1 Data Mining
2.4.2 Data Analysis
2.4.3 Data Validation
2.4.4 Research Approach
2.5 Research Sources
2.5.1 Primary Research Sources
2.5.2 Secondary Research Sources
2.5.3 Assumptions
3 Market Trend Analysis
3.1 Introduction
3.2 Drivers
3.3 Restraints
3.4 Opportunities
3.5 Threats
3.6 Application Analysis
3.7 End User Analysis
3.8 Emerging Markets
3.9 Impact of Covid-19
4 Porters Five Force Analysis
4.1 Bargaining power of suppliers
4.2 Bargaining power of buyers
4.3 Threat of substitutes
4.4 Threat of new entrants
4.5 Competitive rivalry
5 Global Automated Machine Learning (AutoML) Market, By Offering
5.1 Introduction
5.2 Platform
5.3 Service
6 Global Automated Machine Learning (AutoML) Market, By Deployment Type
6.1 Introduction
6.2 Cloud
6.3 On-Premises
7 Global Automated Machine Learning (AutoML) Market, By Automation Type
7.1 Introduction
7.2 Feature Engineering
7.3 Data Processing
7.4 Hyperparameter Optimization & Tuning
7.5 Model Ensembling
7.6 Model Selection
7.7 Visualization
8 Global Automated Machine Learning (AutoML) Market, By Enterprise Size
8.1 Introduction
8.2 Small Enterprise
8.3 Medium Enterprise
8.4 Large Enterprise
9 Global Automated Machine Learning (AutoML) Market, By Application
9.1 Introduction
9.2 Fraud Detection
9.3 Medical Testing
9.4 Sales & Marketing Management
9.5 Transport Optimization
9.6 Other Applications
10 Global Automated Machine Learning (AutoML) Market, By End User
10.1 Introduction
10.2 BFSI
10.3 Healthcare
10.4 IT & Telecom
10.5 Retail
10.6 E-Commerce
10.7 Manufacturing
10.8 Government
10.9 Other End Users
11 Global Automated Machine Learning (AutoML) Market, By Geography
11.1 Introduction
11.2 North America
11.2.1 US
11.2.2 Canada
11.2.3 Mexico
11.3 Europe
11.3.1 Germany
11.3.2 UK
11.3.3 Italy
11.3.4 France
11.3.5 Spain
11.3.6 Rest of Europe
11.4 Asia Pacific
11.4.1 Japan
11.4.2 China
11.4.3 India
11.4.4 Australia
11.4.5 New Zealand
11.4.6 South Korea
11.4.7 Rest of Asia Pacific
11.5 South America
11.5.1 Argentina
11.5.2 Brazil
11.5.3 Chile
11.5.4 Rest of South America
11.6 Middle East & Africa
11.6.1 Saudi Arabia
11.6.2 UAE
11.6.3 Qatar
11.6.4 South Africa
11.6.5 Rest of Middle East & Africa
12 Key Developments
12.1 Agreements, Partnerships, Collaborations and Joint Ventures
12.2 Acquisitions & Mergers
12.3 New Product Launch
12.4 Expansions
12.5 Other Key Strategies
13 Company Profiling
13.1 Amazon Web Services Inc
13.2 DataRobot Inc.
13.3 Qlik Technologies Inc
13.4 Microsoft Corporation
13.5 dotData Inc
13.6 Gnosis DA S.A.
13.7 SAS Institute Inc
13.8 Google LLC
13.9 H2O.ai Inc
13.10 TAZI AI
13.11 RapidMiner
13.12 Squark
13.13 BigML Inc
13.14 Determined.ai Inc
13.15 Dataiku
13.16 IBM Corporation
13.17 EdgeVerve Systems Limited
13.18 Oracle
13.19 Enhencer LLC
List of Tables
1 Global Automated Machine Learning (AutoML) Market Outlook, By Region (2020-2028) ($MN)
2 Global Automated Machine Learning (AutoML) Market Outlook, By Offering (2020-2028) ($MN)
3 Global Automated Machine Learning (AutoML) Market Outlook, By Platform (2020-2028) ($MN)
4 Global Automated Machine Learning (AutoML) Market Outlook, By Service (2020-2028) ($MN)
5 Global Automated Machine Learning (AutoML) Market Outlook, By Deployment Type (2020-2028) ($MN)
6 Global Automated Machine Learning (AutoML) Market Outlook, By Cloud (2020-2028) ($MN)
7 Global Automated Machine Learning (AutoML) Market Outlook, By On-Premises (2020-2028) ($MN)
8 Global Automated Machine Learning (AutoML) Market Outlook, By Automation Type (2020-2028) ($MN)
9 Global Automated Machine Learning (AutoML) Market Outlook, By Feature Engineering (2020-2028) ($MN)
10 Global Automated Machine Learning (AutoML) Market Outlook, By Data Processing (2020-2028) ($MN)
11 Global Automated Machine Learning (AutoML) Market Outlook, By Hyperparameter Optimization & Tuning (2020-2028) ($MN)
12 Global Automated Machine Learning (AutoML) Market Outlook, By Model Ensembling (2020-2028) ($MN)
13 Global Automated Machine Learning (AutoML) Market Outlook, By Model Selection (2020-2028) ($MN)
14 Global Automated Machine Learning (AutoML) Market Outlook, By Visualization (2020-2028) ($MN)
15 Global Automated Machine Learning (AutoML) Market Outlook, By Enterprise Size (2020-2028) ($MN)
16 Global Automated Machine Learning (AutoML) Market Outlook, By Small Enterprise (2020-2028) ($MN)
17 Global Automated Machine Learning (AutoML) Market Outlook, By Medium Enterprise (2020-2028) ($MN)
18 Global Automated Machine Learning (AutoML) Market Outlook, By Large Enterprise (2020-2028) ($MN)
19 Global Automated Machine Learning (AutoML) Market Outlook, By Application (2020-2028) ($MN)
20 Global Automated Machine Learning (AutoML) Market Outlook, By Fraud Detection (2020-2028) ($MN)
21 Global Automated Machine Learning (AutoML) Market Outlook, By Medical Testing (2020-2028) ($MN)
22 Global Automated Machine Learning (AutoML) Market Outlook, By Sales & Marketing Management (2020-2028) ($MN)
23 Global Automated Machine Learning (AutoML) Market Outlook, By Transport Optimization (2020-2028) ($MN)
24 Global Automated Machine Learning (AutoML) Market Outlook, By Other Applications (2020-2028) ($MN)
25 Global Automated Machine Learning (AutoML) Market Outlook, By End User (2020-2028) ($MN)
26 Global Automated Machine Learning (AutoML) Market Outlook, By BFSI (2020-2028) ($MN)
27 Global Automated Machine Learning (AutoML) Market Outlook, By Healthcare (2020-2028) ($MN)
28 Global Automated Machine Learning (AutoML) Market Outlook, By IT & Telecom (2020-2028) ($MN)
29 Global Automated Machine Learning (AutoML) Market Outlook, By Retail (2020-2028) ($MN)
30 Global Automated Machine Learning (AutoML) Market Outlook, By E-Commerce (2020-2028) ($MN)
31 Global Automated Machine Learning (AutoML) Market Outlook, By Manufacturing (2020-2028) ($MN)
32 Global Automated Machine Learning (AutoML) Market Outlook, By Government (2020-2028) ($MN)
33 Global Automated Machine Learning (AutoML) Market Outlook, By Other End Users (2020-2028) ($MN)
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.
List of Figures
RESEARCH METHODOLOGY
We at ‘Stratistics’ opt for an extensive research approach which involves data mining, data validation, and data analysis. The various research sources include in-house repository, secondary research, competitor’s sources, social media research, client internal data, and primary research.
Our team of analysts prefers the most reliable and authenticated data sources in order to perform the comprehensive literature search. With access to most of the authenticated data bases our team highly considers the best mix of information through various sources to obtain extensive and accurate analysis.
Each report takes an average time of a month and a team of 4 industry analysts. The time may vary depending on the scope and data availability of the desired market report. The various parameters used in the market assessment are standardized in order to enhance the data accuracy.
Data Mining
The data is collected from several authenticated, reliable, paid and unpaid sources and is filtered depending on the scope & objective of the research. Our reports repository acts as an added advantage in this procedure. Data gathering from the raw material suppliers, distributors and the manufacturers is performed on a regular basis, this helps in the comprehensive understanding of the products value chain. Apart from the above mentioned sources the data is also collected from the industry consultants to ensure the objective of the study is in the right direction.
Market trends such as technological advancements, regulatory affairs, market dynamics (Drivers, Restraints, Opportunities and Challenges) are obtained from scientific journals, market related national & international associations and organizations.
Data Analysis
From the data that is collected depending on the scope & objective of the research the data is subjected for the analysis. The critical steps that we follow for the data analysis include:
- Product Lifecycle Analysis
- Competitor analysis
- Risk analysis
- Porters Analysis
- PESTEL Analysis
- SWOT Analysis
The data engineering is performed by the core industry experts considering both the Marketing Mix Modeling and the Demand Forecasting. The marketing mix modeling makes use of multiple-regression techniques to predict the optimal mix of marketing variables. Regression factor is based on a number of variables and how they relate to an outcome such as sales or profits.
Data Validation
The data validation is performed by the exhaustive primary research from the expert interviews. This includes telephonic interviews, focus groups, face to face interviews, and questionnaires to validate our research from all aspects. The industry experts we approach come from the leading firms, involved in the supply chain ranging from the suppliers, distributors to the manufacturers and consumers so as to ensure an unbiased analysis.
We are in touch with more than 15,000 industry experts with the right mix of consultants, CEO's, presidents, vice presidents, managers, experts from both supply side and demand side, executives and so on.
The data validation involves the primary research from the industry experts belonging to:
- Leading Companies
- Suppliers & Distributors
- Manufacturers
- Consumers
- Industry/Strategic Consultants
Apart from the data validation the primary research also helps in performing the fill gap research, i.e. providing solutions for the unmet needs of the research which helps in enhancing the reports quality.
For more details about research methodology, kindly write to us at info@strategymrc.com
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