Product ID: UCMIG45C2085
Automated Machine Learning (AutoML) Market Size, Share, Growth Analysis, By Offering:(Solutions), By Type(Platform, Software), By Deployment(Cloud, On-premises), By Application:(Data Processing, Feature Engineering), By Vertical:(Banking, financial services), By Region:(North America, US) - Industry Forecast 2024-2031
Report ID:
UCMIG45C2085 |
Region:
Global |
Published Date: Upcoming |
Pages:
165
| Tables: 55 | Figures: 60
Automated Machine Learning (AutoML) Market Insights
Market Overview:
At a CAGR of roughly 44.6% from 2023 to 2028, the global Automated Machine Learning (AutoML) Market size increased from $1.0 billion in 2023 to an estimated $6.4 billion by the end of 2028. The growing demand for personalized product recommendations and improved customer satisfaction through automated machine learning, as well as the rising demand for accurate fraud detection, rising data volume and complexity, and the rising need to transform businesses through intelligent automation using automated machine learning, are the main factors driving the market growth for this technology.
Automated Machine Learning (AutoML) Market, Forecast & Y-O-Y Growth Rate, 2020 - 2028
To get more reports on the above market click here to
GET FREE SAMPLE
GET FREE SAMPLE
This report is being written to illustrate the market opportunity by region and by segments, indicating opportunity areas for the vendors to tap upon. To estimate the opportunity, it was very important to understand the current market scenario and the way it will grow in future.
Production and consumption patterns are being carefully compared to forecast the market. Other factors considered to forecast the market are the growth of the adjacent market, revenue growth of the key market vendors, scenario-based analysis, and market segment growth.
The market size was determined by estimating the market through a top-down and bottom-up approach, which was further validated with industry interviews. Considering the nature of the market we derived the Data Processing & Outsourced Services by segment aggregation, the contribution of the Data Processing & Outsourced Services in Software & Services and vendor share.
To determine the growth of the market factors such as drivers, trends, restraints, and opportunities were identified, and the impact of these factors was analyzed to determine the market growth. To understand the market growth in detail, we have analyzed the year-on-year growth of the market. Also, historic growth rates were compared to determine growth patterns.
Segmentation Analysis:
The Automated Machine Learning (AutoML) Market is segmented by Offering:, Type, Deployment, Application:, Vertical:, Region:. We are analyzing the market of these segments to identify which segment is the largest now and in the future, which segment has the highest growth rate, and the segment which offers the opportunity in the future.
Automated Machine Learning (AutoML) Market Basis Point Share Analysis, 2021 Vs. 2028
To get detailed analysis on all segments
BUY NOW
BUY NOW
- Based on Offering: the market is segmented as, Solutions
- Based on Type the market is segmented as, Platform, Software
- Based on Deployment the market is segmented as, Cloud, On-premises, Services, Consulting Services, Deployment & Integration, Training, Support, and Maintenance
- Based on Application: the market is segmented as, Data Processing, Feature Engineering, Model Selection, Hyperparameter Optimization & Tuning, Model Ensembling, Other Applications
- Based on Vertical: the market is segmented as, Banking, financial services, and insurance, Retail & eCommerce, Healthcare & life sciences, IT & ITeS, Telecommunications, Government & defense, Manufacturing, Automotive, Transportations, and Logistics, Media & Entertainment, Other Verticals
- Based on Region: the market is segmented as, North America, US, Canada, Europe, UK, Germany, France, Italy, Spain, Nordic, Rest of Europe, Asia Pacific, China, Japan, India, South Korea, Australia & New Zealand, ASEAN, Rest of Asia Pacific, Middle East & Africa, UAE, Kingdom of Saudi Arabia, Israel, Turkey, South Africa, Rest of the Middle East & Africa, Latin America, Brazil, Mexico, Argentina, Rest of Latin America, KEY MARKET PLAYERS, IBM, Oracle, Microsoft, ServiceNow, Google, Baidu, AWS, Alteryx, Salesforce, Altair, Teradata, H2O.ai, DataRobot, BigML, Databricks, Dataiku, Alibaba Cloud, Appier, Squark, Aible, Datafold, Boost.ai, Tazi.ai, Akkio, Valohai, dotData, Qlik, Mathworks, HPE
Regional Analysis:
Automated Machine Learning (AutoML) Market is being analyzed by North America, Europe, Asia-Pacific (APAC), Latin America (LATAM), Middle East & Africa (MEA) regions. Key countries including the U.S., Canada, Germany, France, UK, Italy, Spain, China, India, Japan, Brazil, GCC Countries, and South Africa among others were analyzed considering various micro and macro trends.
Automated Machine Learning (AutoML) Market Attractiveness Analysis, By Region 2020-2028
To know more about the market opportunities by region and country, click here to
REQUEST FREE CUSTOMIZATION
REQUEST FREE CUSTOMIZATION
Automated Machine Learning (AutoML) Market : Risk Analysis
SkyQuest's expert analysts have conducted a risk analysis to understand the impact of external extremities on Automated Machine Learning (AutoML) Market. We analyzed how geopolitical influence, natural disasters, climate change, legal scenario, economic impact, trade & economic policies, social & ethnic concerns, and demographic changes might affect Automated Machine Learning (AutoML) Market's supply chain, distribution, and total revenue growth.
Competitive landscaping:
To understand the competitive landscape, we are analyzing key Automated Machine Learning (AutoML) Market vendors in the market. To understand the competitive rivalry, we are comparing the revenue, expenses, resources, product portfolio, region coverage, market share, key initiatives, product launches, and any news related to the Automated Machine Learning (AutoML) Market.
To validate our hypothesis and validate our findings on the market ecosystem, we are also conducting a detailed porter's five forces analysis. Competitive Rivalry, Supplier Power, Buyer Power, Threat of Substitution, and Threat of New Entry each force is analyzed by various parameters governing those forces.
Key Players Covered in the Report:
- Automated Machine Learning (AutoML) Market size was worth approximately $1.0 billion in 2023 and is expected to generate revenue around $6.4 billion by the end of 2028 growing at a CAGR of around 44.6% between 2023 to 2028.
- Transfer learning is an important aspect of AutoML that leverages pre-trained models to improve the performance of new models. By transferring the knowledge learned from one task to another, businesses create more accurate models with less training data and reduce the time and cost required to build high-performing models.
- Driver: Rising need to transform businesses with Intelligent automation using AutoML
- As businesses increasingly rely on data to drive decision-making and improve operational efficiency, a growing demand for intelligent business processes has been growing. These processes leverage machine learning algorithms to automate decision-making and optimize business operations, leading to improved performance and increased profitability. By leveraging AutoML, businesses can streamline operations, reduce costs, and improve performance, ultimately resulting in a competitive advantage. According to a report by industry experts, it is found that AI-driven automation can improve productivity by up to 40%. Automated Machine Learning Market can help businesses achieve these kinds of results by automating the development and deployment of machine learning models. By using AutoML, businesses can quickly and efficiently develop predictive models that can be integrated into existing business processes. These models can then be used to automate decision-making, optimize processes, and improve performance. Additionally, AutoML can help businesses identify new opportunities for optimization and improvement that were previously difficult to detect. By analyzing large volumes of data, AutoML can identify patterns and trends, enabling businesses to make data-driven decisions to improve performance and drive growth.
- Restraint: Machine learning tools are being slowly adopted
- The slow adoption of automated machine learning (AutoML) tools is a significant restraint hindering the growth of the AutoML market. Despite the numerous benefits AutoML offers, including increased efficiency, accuracy, and scalability, many organizations hesitate to adopt this technology. One of the primary reasons for this slow adoption is a lack of awareness about Automated Machine Learning (AutoML) Market and its capabilities. 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. This lack of awareness is a significant barrier to its adoption, as many business leaders and decision-makers may not be familiar with the benefits of AutoML and the impact it can have on their business.
- Another factor contributing to this slow adoption is a shortage of skilled data scientists and machine learning experts. According to a report by an industry expert, there will be a shortage of up to 250,000 data scientists by 2024. This shortage of skilled experts can make it challenging for organizations to develop and deploy machine learning models, leading to slower adoption rates. Concerns about the transparency and interpretability of machine learning models are also holding back adoption. This lack of transparency can be a barrier to adoption in industries such as healthcare and finance, where decisions based on machine learning models can have significant consequences.
- Opportunity: Seizing opportunities for faster decision-making and cost savings
- The increased accessibility of machine learning presents a significant opportunity for the AutoML market. Historically, machine learning has been highly specialized, requiring extensive statistics, programming, and data analysis expertise. However, with the development of AutoML tools, businesses no longer need to have a team of data scientists and machine learning experts to build and deploy AI solutions. Instead, AutoML tools enable businesses to democratize the use of machine learning, making it more accessible to a broader range of users and use cases. By using AutoML tools, businesses can quickly build and deploy predictive models that can analyze large amounts of data and identify patterns, anomalies, and insights that may not be apparent to the human eye. For instance, AutoML models can help businesses to predict customer behavior, optimize pricing strategies, and identify opportunities for process improvement.
- Moreover, the increased accessibility of machine learning can also lead to significant cost savings for businesses. By using AutoML tools, businesses can reduce the costs associated with hiring specialized talent and investing in expensive infrastructure. Additionally, the faster development and deployment of AI solutions can result in cost savings by increasing operational efficiency and improving decision-making. Furthermore, the increased accessibility of machine learning can also lead to new business innovations and opportunities. As more businesses adopt AutoML tools, there is likely to be a proliferation of new use cases and applications, leading to increased innovation and growth in the market. Additionally, the democratization of machine learning can enable businesses to tap into new markets and expand their offerings, leading to increased revenue and market share.
- Challenge: Increasing shortage of skilled talent
- One of the biggest challenges facing the AutoML market is the shortage of skilled talent. AutoML platforms require individuals with a strong machine learning, data science, and programming background. However, the demand for these skills has far outpaced the supply, leading to a significant talent shortage in the industry. As a result, organizations struggle to find the right talent to build, deploy, and maintain AutoML models. According to a report by LinkedIn, data scientists and machine learning engineers are among the top emerging jobs in the technology sector. However, the shortage of skilled talent has led to fierce competition among organizations for a limited pool of candidates. The LinkedIn report found that data scientist positions take an average of 42 days to fill, highlighting the challenges of finding qualified candidates.
- The rapid pace of technological advancements further compounds the shortage of skilled talent. As new algorithms and techniques are developed, it is essential for individuals working with AutoML platforms to continually upskill and stay up to date with the latest advancements in the field. This requires ongoing training and professional development, which can be costly and time-consuming. Furthermore, the shortage of skilled talent is not limited to data scientists and machine learning engineers. AutoML platforms also require individuals with expertise in areas such as data management, data visualization, and cloud computing. The talent shortage in these areas can also impact the successful implementation and adoption of AutoML solutions.
- By application, data processing to account for the largest market size during the forecast period
- AutoML can be applied to automate various aspects of data processing, including data cleaning, data normalization, and data transformation. Automated Machine Learning (AutoML) Market can automate the process of detecting and correcting errors in the data. This includes identifying missing values, correcting data formatting errors, and removing outliers that could affect the accuracy of machine learning models. AutoML involves techniques such as standardization and normalization, which can be automatically applied to the data. In transformation, data can be transformed to a more appropriate format, reducing the risk of errors and inconsistencies with the help of AutoML. AutoML can also integrate data from multiple sources, which is often a time-consuming and complex task. This includes techniques such as data merging and joining. By automating the tasks, AutoML can reduce the time and effort required for manual data processing, improving the quality and accuracy of the resulting data.
- By deployment, cloud to account for the largest market size during the forecast period
- The adoption of cloud computing has increased over the past few years, with internet connections becoming more reliable and remote work becoming the norm. Cloud-based AutoML solutions offer greater flexibility and scalability than on-premises solutions, as they can be easily scaled up or down as needed to accommodate changes in workload or data volume. Cloud-based solutions also typically offer a pay-as-you-go pricing model, which can be more cost-effective for organizations with variable workloads. Installing and supporting traditional software can be very challenging for a decentralized team. However, with cloud-based software, anyone can access the solution remotely. For SMEs, cloud is particularly useful as it provides full functionality at a reasonable rate and requires no upfront investment. Due to its commercial benefits and the world of possibilities, cloud technology has become a popular model for modern automation businesses.
- By offering, solutions to account for the largest market size during the forecast period
- AutoML solutions are designed to automate the tasks involved in developing and deploying machine learning models. This makes it easier for organizations to leverage the power of machine learning without requiring significant expertise in data science or machine learning. AutoML solutions are becoming an increasingly important tool for organizations looking to leverage the power of machine learning to gain insights from their data and make better decisions. By automating many tedious and time-consuming tasks involved in model development and deployment, AutoML platforms can help organizations accelerate their digital transformation and unlock new opportunities for growth and innovation. AutoML solutions are available in different types and deployment options. By type, AutoML solutions can be classified into AutoML platforms and AutoML software. By deployment, AutoML solutions can be deployed on-premises or on cloud.
- North America to account for the largest market size during the forecast period
- North America is expected to have the largest market share in Automated Machine Learning. North America has been a major contributor to the development and growth of the Automated Machine Learning market. The US is one of the most developed countries in the region. AutoML is a rapidly growing market in the US, with several key players offering solutions that range from fully automated platforms to ones that assist data scientists in building machine learning models. The market is being driven by the need for faster and more efficient ways to build and deploy machine learning models, as well as the increasing demand for artificial intelligence solutions in various industries. In recent years, there has been a significant increase in the adoption of AutoML solutions in the US, especially in industries such as healthcare, finance, and retail. Healthcare providers are using AutoML to analyze medical images and identify patterns in patient data, while financial institutions are using it to detect fraudulent transactions and assess credit risk. Retailers are using AutoML to personalize recommendations and improve customer engagement.
- Recent 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 February 2023, AWS launched new features for Amazon SageMaker Autopilot, a tool for automating the machine learning (ML) model creation process. The new features include the ability to select specific algorithms for the training and experiment stages, allowing data scientists more control over the ML model creation process.
- 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 February 2022, AWS partnered with Maple Leaf Sports & Entertainment, which enables MLSE to assist its teams and lines of business. MLSE also employs AWS's extensive cloud capabilities, including AutoML, advanced analytics, computing, database, and storage services.
- In December 2020, Salesforce and Google Cloud announced a collaboration to integrate Google Cloud's automl tools with Salesforce's Customer 360 platform. The collaboration aimed to help businesses build a complete view of their customers and use AI to personalize their experiences.
- KEY MARKET SEGMENTS
- By Offering:
- Solutions
- By Type
- Platform
- Software
- By Deployment
- Cloud
- On-premises
- Services
- Consulting Services
- Deployment & Integration
- Training, Support, and Maintenance
- By Application:
- Data Processing
- Feature Engineering
- Model Selection
- Hyperparameter Optimization & Tuning
- Model Ensembling
- Other Applications
- By Vertical:
- Banking, financial services, and insurance
- Retail & eCommerce
- Healthcare & life sciences
- IT & ITeS
- Telecommunications
- Government & defense
- Manufacturing
- Automotive, Transportations, and Logistics
- Media & Entertainment
- Other Verticals
- By Region:
- North America
- US
- Canada
- Europe
- UK
- Germany
- France
- Italy
- Spain
- Nordic
- Rest of Europe
- Asia Pacific
- China
- Japan
- India
- South Korea
- Australia & New Zealand
- ASEAN
- Rest of Asia Pacific
- Middle East & Africa
- UAE
- Kingdom of Saudi Arabia
- Israel
- Turkey
- South Africa
- Rest of the Middle East & Africa
- Latin America
- Brazil
- Mexico
- Argentina
- Rest of Latin America
- KEY MARKET PLAYERS
- IBM
- Oracle
- Microsoft
- ServiceNow
- Baidu
- AWS
- Alteryx
- Salesforce
- Altair
- Teradata
- H2O.ai
- DataRobot
- BigML
- Databricks
- Dataiku
- Alibaba Cloud
- Appier
- Squark
- Aible
- Datafold
- Boost.ai
- Tazi.ai
- Akkio
- Valohai
- dotData
- Qlik
- Mathworks
- HPE
- SparkCognition
SkyQuest's Expertise:
The Automated Machine Learning (AutoML) Market is being analyzed by SkyQuest's analysts with the help of 20+ scheduled Primary interviews from both the demand and supply sides. We have already invested more than 250 hours on this report and are still refining our date to provide authenticated data to your readers and clients. Exhaustive primary and secondary research is conducted to collect information on the market, peer market, and parent market.
Our cross-industry experts and revenue-impact consultants at SkyQuest enable our clients to convert market intelligence into actionable, quantifiable results through personalized engagement.
Scope Of Report
| Report Attribute | Details |
|---|---|
| The base year for estimation | 2021 |
| Historical data | 2016 – 2022 |
| Forecast period | 2022 – 2028 |
| Report coverage | Revenue forecast, volume forecast, company ranking, competitive landscape, growth factors, and trends, Pricing Analysis |
| Segments covered |
|
| Regional scope | North America, Europe, Asia-Pacific (APAC), Latin America (LATAM), Middle East & Africa (MEA) |
| Country scope | U.S., Canada, Germany, France, UK, Italy, Spain, China, India, Japan, Brazil, GCC Countries, South Africa |
| Key companies profiled |
|
| Customization scope | Free report customization (15% Free customization) with purchase. Addition or alteration to country, regional & segment scope. |
| Pricing and purchase options | Reap the benefits of customized purchase options to fit your specific research requirements. |
Objectives of the Study
- To forecast the market size, in terms of value, for various segments with respect to five main regions, namely, North America, Europe, Asia-Pacific (APAC), Latin America (LATAM), Middle East & Africa (MEA)
- To provide detailed information regarding the major factors influencing the growth of the Market (drivers, restraints, opportunities, and challenges)
- To strategically analyze the micro markets with respect to the individual growth trends, future prospects, and contribution to the total market
- To provide a detailed overview of the value chain and analyze market trends with the Porter's five forces analysis
- To analyze the opportunities in the market for various stakeholders by identifying the high-growth Segments
- To identify the key players and comprehensively analyze their market position in terms of ranking and core competencies, along with detailing the competitive landscape for the market leaders
- To analyze competitive development such as joint ventures, mergers and acquisitions, new product launches and development, and research and development in the market
What does this Report Deliver?
- Market Estimation for 20+ Countries
- Historical data coverage: 2016 to 2022
- Growth projections: 2022 to 2028
- SkyQuest's premium market insights: Innovation matrix, IP analysis, Production Analysis, Value chain analysis, Technological trends, and Trade analysis
- Customization on Segments, Regions, and Company Profiles
- 100+ tables, 150+ Figures, 10+ matrix
- Global and Country Market Trends
- Comprehensive Mapping of Industry Parameters
- Attractive Investment Proposition
- Competitive Strategies Adopted by Leading Market Participants
- Market drivers, restraints, opportunities, and its impact on the market
- Regulatory scenario, regional dynamics, and insights of leading countries in each region
- Segment trends analysis, opportunity, and growth
- Opportunity analysis by region and country
- Porter's five force analysis to know the market's condition
- Pricing analysis
- Parent market analysis
- Product portfolio benchmarking
Table Of Content
Executive Summary
Market overview
- Exhibit: Executive Summary – Chart on Market Overview
- Exhibit: Executive Summary – Data Table on Market Overview
- Exhibit: Executive Summary – Chart on Automated Machine Learning (AutoML) Market Characteristics
- Exhibit: Executive Summary – Chart on Market by Geography
- Exhibit: Executive Summary – Chart on Market Segmentation
- Exhibit: Executive Summary – Chart on Incremental Growth
- Exhibit: Executive Summary – Data Table on Incremental Growth
- Exhibit: Executive Summary – Chart on Vendor Market Positioning
Parent Market Analysis
Market overview
Market size
- Market Dynamics
- Exhibit: Impact analysis of DROC, 2021
- Drivers
- Opportunities
- Restraints
- Challenges
- Exhibit: Impact analysis of DROC, 2021
- SWOT Analysis
KEY MARKET INSIGHTS
- Technology Analysis
- (Exhibit: Data Table: Name of technology and details)
- Pricing Analysis
- (Exhibit: Data Table: Name of technology and pricing details)
- Supply Chain Analysis
- (Exhibit: Detailed Supply Chain Presentation)
- Value Chain Analysis
- (Exhibit: Detailed Value Chain Presentation)
- Ecosystem Of the Market
- Exhibit: Parent Market Ecosystem Market Analysis
- Exhibit: Market Characteristics of Parent Market
- IP Analysis
- (Exhibit: Data Table: Name of product/technology, patents filed, inventor/company name, acquiring firm)
- Trade Analysis
- (Exhibit: Data Table: Import and Export data details)
- Startup Analysis
- (Exhibit: Data Table: Emerging startups details)
- Raw Material Analysis
- (Exhibit: Data Table: Mapping of key raw materials)
- Innovation Matrix
- (Exhibit: Positioning Matrix: Mapping of new and existing technologies)
- Pipeline product Analysis
- (Exhibit: Data Table: Name of companies and pipeline products, regional mapping)
- Macroeconomic Indicators
COVID IMPACT
- Introduction
- Impact On Economy—scenario Assessment
- Exhibit: Data on GDP - Year-over-year growth 2016-2022 (%)
- Revised Market Size
- Exhibit: Data Table on Automated Machine Learning (AutoML) Market size and forecast 2021-2027 ($ million)
- Impact Of COVID On Key Segments
- Exhibit: Data Table on Segment Market size and forecast 2021-2027 ($ million)
- COVID Strategies By Company
- Exhibit: Analysis on key strategies adopted by companies
MARKET DYNAMICS & OUTLOOK
- Market Dynamics
- Exhibit: Impact analysis of DROC, 2021
- Drivers
- Opportunities
- Restraints
- Challenges
- Exhibit: Impact analysis of DROC, 2021
- Regulatory Landscape
- Exhibit: Data Table on regulation from different region
- SWOT Analysis
- Porters Analysis
- Competitive rivalry
- Exhibit: Competitive rivalry Impact of key factors, 2021
- Threat of substitute products
- Exhibit: Threat of Substitute Products Impact of key factors, 2021
- Bargaining power of buyers
- Exhibit: buyers bargaining power Impact of key factors, 2021
- Threat of new entrants
- Exhibit: Threat of new entrants Impact of key factors, 2021
- Bargaining power of suppliers
- Exhibit: Threat of suppliers bargaining power Impact of key factors, 2021
- Competitive rivalry
- Skyquest special insights on future disruptions
- Political Impact
- Economic impact
- Social Impact
- Technical Impact
- Environmental Impact
- Legal Impact
Market Size by Region
- Chart on Market share by geography 2021-2027 (%)
- Data Table on Market share by geography 2021-2027(%)
- North America
- Chart on Market share by country 2021-2027 (%)
- Data Table on Market share by country 2021-2027(%)
- USA
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Canada
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Europe
- Chart on Market share by country 2021-2027 (%)
- Data Table on Market share by country 2021-2027(%)
- Germany
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Spain
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- France
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- UK
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Rest of Europe
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Asia Pacific
- Chart on Market share by country 2021-2027 (%)
- Data Table on Market share by country 2021-2027(%)
- China
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- India
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Japan
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- South Korea
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Rest of Asia Pacific
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Latin America
- Chart on Market share by country 2021-2027 (%)
- Data Table on Market share by country 2021-2027(%)
- Brazil
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Rest of South America
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Middle East & Africa (MEA)
- Chart on Market share by country 2021-2027 (%)
- Data Table on Market share by country 2021-2027(%)
- GCC Countries
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- South Africa
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
- Rest of MEA
- Exhibit: Chart on Market share 2021-2027 (%)
- Exhibit: Market size and forecast 2021-2027 ($ million)
KEY COMPANY PROFILES
- Competitive Landscape
- Total number of companies covered
- Exhibit: companies covered in the report, 2021
- Top companies market positioning
- Exhibit: company positioning matrix, 2021
- Top companies market Share
- Exhibit: Pie chart analysis on company market share, 2021(%)
- Total number of companies covered
- Automated Machine Learning (AutoML) Market size was worth approximately $1.0 billion in 2023 and is expected to generate revenue around $6.4 billion by the end of 2028 growing at a CAGR of around 44.6% between 2023 to 2028.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Transfer learning is an important aspect of AutoML that leverages pre-trained models to improve the performance of new models. By transferring the knowledge learned from one task to another, businesses create more accurate models with less training data and reduce the time and cost required to build high-performing models.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Driver: Rising need to transform businesses with Intelligent automation using AutoML
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- As businesses increasingly rely on data to drive decision-making and improve operational efficiency, a growing demand for intelligent business processes has been growing. These processes leverage machine learning algorithms to automate decision-making and optimize business operations, leading to improved performance and increased profitability. By leveraging AutoML, businesses can streamline operations, reduce costs, and improve performance, ultimately resulting in a competitive advantage. According to a report by industry experts, it is found that AI-driven automation can improve productivity by up to 40%. Automated Machine Learning Market can help businesses achieve these kinds of results by automating the development and deployment of machine learning models. By using AutoML, businesses can quickly and efficiently develop predictive models that can be integrated into existing business processes. These models can then be used to automate decision-making, optimize processes, and improve performance. Additionally, AutoML can help businesses identify new opportunities for optimization and improvement that were previously difficult to detect. By analyzing large volumes of data, AutoML can identify patterns and trends, enabling businesses to make data-driven decisions to improve performance and drive growth.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Restraint: Machine learning tools are being slowly adopted
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- The slow adoption of automated machine learning (AutoML) tools is a significant restraint hindering the growth of the AutoML market. Despite the numerous benefits AutoML offers, including increased efficiency, accuracy, and scalability, many organizations hesitate to adopt this technology. One of the primary reasons for this slow adoption is a lack of awareness about Automated Machine Learning (AutoML) Market and its capabilities. 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. This lack of awareness is a significant barrier to its adoption, as many business leaders and decision-makers may not be familiar with the benefits of AutoML and the impact it can have on their business.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Another factor contributing to this slow adoption is a shortage of skilled data scientists and machine learning experts. According to a report by an industry expert, there will be a shortage of up to 250,000 data scientists by 2024. This shortage of skilled experts can make it challenging for organizations to develop and deploy machine learning models, leading to slower adoption rates. Concerns about the transparency and interpretability of machine learning models are also holding back adoption. This lack of transparency can be a barrier to adoption in industries such as healthcare and finance, where decisions based on machine learning models can have significant consequences.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Opportunity: Seizing opportunities for faster decision-making and cost savings
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- The increased accessibility of machine learning presents a significant opportunity for the AutoML market. Historically, machine learning has been highly specialized, requiring extensive statistics, programming, and data analysis expertise. However, with the development of AutoML tools, businesses no longer need to have a team of data scientists and machine learning experts to build and deploy AI solutions. Instead, AutoML tools enable businesses to democratize the use of machine learning, making it more accessible to a broader range of users and use cases. By using AutoML tools, businesses can quickly build and deploy predictive models that can analyze large amounts of data and identify patterns, anomalies, and insights that may not be apparent to the human eye. For instance, AutoML models can help businesses to predict customer behavior, optimize pricing strategies, and identify opportunities for process improvement.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Moreover, the increased accessibility of machine learning can also lead to significant cost savings for businesses. By using AutoML tools, businesses can reduce the costs associated with hiring specialized talent and investing in expensive infrastructure. Additionally, the faster development and deployment of AI solutions can result in cost savings by increasing operational efficiency and improving decision-making. Furthermore, the increased accessibility of machine learning can also lead to new business innovations and opportunities. As more businesses adopt AutoML tools, there is likely to be a proliferation of new use cases and applications, leading to increased innovation and growth in the market. Additionally, the democratization of machine learning can enable businesses to tap into new markets and expand their offerings, leading to increased revenue and market share.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Challenge: Increasing shortage of skilled talent
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- One of the biggest challenges facing the AutoML market is the shortage of skilled talent. AutoML platforms require individuals with a strong machine learning, data science, and programming background. However, the demand for these skills has far outpaced the supply, leading to a significant talent shortage in the industry. As a result, organizations struggle to find the right talent to build, deploy, and maintain AutoML models. According to a report by LinkedIn, data scientists and machine learning engineers are among the top emerging jobs in the technology sector. However, the shortage of skilled talent has led to fierce competition among organizations for a limited pool of candidates. The LinkedIn report found that data scientist positions take an average of 42 days to fill, highlighting the challenges of finding qualified candidates.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- The rapid pace of technological advancements further compounds the shortage of skilled talent. As new algorithms and techniques are developed, it is essential for individuals working with AutoML platforms to continually upskill and stay up to date with the latest advancements in the field. This requires ongoing training and professional development, which can be costly and time-consuming. Furthermore, the shortage of skilled talent is not limited to data scientists and machine learning engineers. AutoML platforms also require individuals with expertise in areas such as data management, data visualization, and cloud computing. The talent shortage in these areas can also impact the successful implementation and adoption of AutoML solutions.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- By application, data processing to account for the largest market size during the forecast period
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- AutoML can be applied to automate various aspects of data processing, including data cleaning, data normalization, and data transformation. Automated Machine Learning (AutoML) Market can automate the process of detecting and correcting errors in the data. This includes identifying missing values, correcting data formatting errors, and removing outliers that could affect the accuracy of machine learning models. AutoML involves techniques such as standardization and normalization, which can be automatically applied to the data. In transformation, data can be transformed to a more appropriate format, reducing the risk of errors and inconsistencies with the help of AutoML. AutoML can also integrate data from multiple sources, which is often a time-consuming and complex task. This includes techniques such as data merging and joining. By automating the tasks, AutoML can reduce the time and effort required for manual data processing, improving the quality and accuracy of the resulting data.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- By deployment, cloud to account for the largest market size during the forecast period
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- The adoption of cloud computing has increased over the past few years, with internet connections becoming more reliable and remote work becoming the norm. Cloud-based AutoML solutions offer greater flexibility and scalability than on-premises solutions, as they can be easily scaled up or down as needed to accommodate changes in workload or data volume. Cloud-based solutions also typically offer a pay-as-you-go pricing model, which can be more cost-effective for organizations with variable workloads. Installing and supporting traditional software can be very challenging for a decentralized team. However, with cloud-based software, anyone can access the solution remotely. For SMEs, cloud is particularly useful as it provides full functionality at a reasonable rate and requires no upfront investment. Due to its commercial benefits and the world of possibilities, cloud technology has become a popular model for modern automation businesses.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- By offering, solutions to account for the largest market size during the forecast period
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- AutoML solutions are designed to automate the tasks involved in developing and deploying machine learning models. This makes it easier for organizations to leverage the power of machine learning without requiring significant expertise in data science or machine learning. AutoML solutions are becoming an increasingly important tool for organizations looking to leverage the power of machine learning to gain insights from their data and make better decisions. By automating many tedious and time-consuming tasks involved in model development and deployment, AutoML platforms can help organizations accelerate their digital transformation and unlock new opportunities for growth and innovation. AutoML solutions are available in different types and deployment options. By type, AutoML solutions can be classified into AutoML platforms and AutoML software. By deployment, AutoML solutions can be deployed on-premises or on cloud.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- North America to account for the largest market size during the forecast period
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- North America is expected to have the largest market share in Automated Machine Learning. North America has been a major contributor to the development and growth of the Automated Machine Learning market. The US is one of the most developed countries in the region. AutoML is a rapidly growing market in the US, with several key players offering solutions that range from fully automated platforms to ones that assist data scientists in building machine learning models. The market is being driven by the need for faster and more efficient ways to build and deploy machine learning models, as well as the increasing demand for artificial intelligence solutions in various industries. In recent years, there has been a significant increase in the adoption of AutoML solutions in the US, especially in industries such as healthcare, finance, and retail. Healthcare providers are using AutoML to analyze medical images and identify patterns in patient data, while financial institutions are using it to detect fraudulent transactions and assess credit risk. Retailers are using AutoML to personalize recommendations and improve customer engagement.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Recent Developments:
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit 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.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- In February 2023, AWS launched new features for Amazon SageMaker Autopilot, a tool for automating the machine learning (ML) model creation process. The new features include the ability to select specific algorithms for the training and experiment stages, allowing data scientists more control over the ML model creation process.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- 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.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- In February 2022, AWS partnered with Maple Leaf Sports & Entertainment, which enables MLSE to assist its teams and lines of business. MLSE also employs AWS's extensive cloud capabilities, including AutoML, advanced analytics, computing, database, and storage services.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- In December 2020, Salesforce and Google Cloud announced a collaboration to integrate Google Cloud's automl tools with Salesforce's Customer 360 platform. The collaboration aimed to help businesses build a complete view of their customers and use AI to personalize their experiences.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- KEY MARKET SEGMENTS
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- By Offering:
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Solutions
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- By Type
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Platform
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Software
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- By Deployment
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Cloud
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- On-premises
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Services
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Consulting Services
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Deployment & Integration
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Training, Support, and Maintenance
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- By Application:
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Data Processing
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Feature Engineering
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Model Selection
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Hyperparameter Optimization & Tuning
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Model Ensembling
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Other Applications
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- By Vertical:
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Banking, financial services, and insurance
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Retail & eCommerce
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Healthcare & life sciences
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- IT & ITeS
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Telecommunications
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Government & defense
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Manufacturing
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Automotive, Transportations, and Logistics
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Media & Entertainment
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Other Verticals
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- By Region:
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- North America
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- US
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Canada
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Europe
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- UK
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Germany
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- France
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Italy
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Spain
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Nordic
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Rest of Europe
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Asia Pacific
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- China
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Japan
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- India
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- South Korea
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Australia & New Zealand
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- ASEAN
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Rest of Asia Pacific
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Middle East & Africa
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- UAE
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Kingdom of Saudi Arabia
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Israel
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Turkey
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- South Africa
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Rest of the Middle East & Africa
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Latin America
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Brazil
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Mexico
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Argentina
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Rest of Latin America
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- KEY MARKET PLAYERS
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- IBM
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Oracle
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Microsoft
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- ServiceNow
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Google
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Baidu
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- AWS
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Alteryx
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Salesforce
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Altair
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Teradata
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- H2O.ai
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- DataRobot
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- BigML
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Databricks
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Dataiku
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Alibaba Cloud
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Appier
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Squark
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Aible
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Datafold
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Boost.ai
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Tazi.ai
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Akkio
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Valohai
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- dotData
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Qlik
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Mathworks
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- HPE
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- SparkCognition
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Methodology
For the Automated Machine Learning (AutoML) Market, our research methodology involved a mixture of primary and secondary data sources. Key steps involved in the research process are listed below:
1. Information Procurement: This stage involved the procurement of Market data or related information via primary and secondary sources. The various secondary sources used included various company websites, annual reports, trade databases, and paid databases such as Hoover's, Bloomberg Business, Factiva, and Avention. Our team did 45 primary interactions Globally which included several stakeholders such as manufacturers, customers, key opinion leaders, etc. Overall, information procurement was one of the most extensive stages in our research process.
2. Information Analysis: This step involved triangulation of data through bottom-up and top-down approaches to estimate and validate the total size and future estimate of the Automated Machine Learning (AutoML) Market.
3. Report Formulation: The final step entailed the placement of data points in appropriate Market spaces in an attempt to deduce viable conclusions.
4. Validation & Publishing: Validation is the most important step in the process. Validation & re-validation via an intricately designed process helped us finalize data points to be used for final calculations. The final Market estimates and forecasts were then aligned and sent to our panel of industry experts for validation of data. Once the validation was done the report was sent to our Quality Assurance team to ensure adherence to style guides, consistency & design.
Analyst Support
Customization Options
With the given market data, our dedicated team of analysts can offer you the following customization options are available for the Automated Machine Learning (AutoML) Market:
Product Analysis: Product matrix, which offers a detailed comparison of the product portfolio of companies.
Regional Analysis: Further analysis of the Automated Machine Learning (AutoML) Market for additional countries.
Competitive Analysis: Detailed analysis and profiling of additional Market players & comparative analysis of competitive products.
Go to Market Strategy: Find the high-growth channels to invest your marketing efforts and increase your customer base.
Innovation Mapping: Identify racial solutions and innovation, connected to deep ecosystems of innovators, start-ups, academics, and strategic partners.
Category Intelligence: Customized intelligence that is relevant to their supply Markets will enable them to make smarter sourcing decisions and improve their category management.
Public Company Transcript Analysis: To improve the investment performance by generating new alpha and making better-informed decisions.
Social Media Listening: To analyze the conversations and trends happening not just around your brand, but around your industry as a whole, and use those insights to make better Marketing decisions.
Automated Machine Learning (AutoML) Market Report Snapshots
Want to customize this report?
Our industry expert will work with you to provide you with customized data in a short amount of time.
REQUEST FREE CUSTOMIZATION
USA (+1) 351-333-4748
