“IBM (US), Oracle (US), Microsoft (US), ServiceNow (US), Google (US), Baidu (China), AWS (US), Alteryx (US), Salesforce (US), Altair (US), Teradata (US), H2O.ai (US), DataRobot (US), BigML (US), Databricks (US), Dataiku (France), Alibaba Cloud (China).”
Automated Machine Learning (AutoML) Market by Offering (Solutions & Services), Application (Data Processing, Model Selection, Hyperparameter Optimization & Tuning, Feature Engineering, Model Ensembling), Vertical and Region – Global Forecast to 2028.
The market for Automated Machine Learning is estimated to grow from USD 1.0 billion in 2023 to USD 6.4 billion by 2028, at a CAGR of 44.6% during the forecast period. Automated machine learning (AutoML) is a subset of artificial intelligence (AI) that enables users to create machine learning applications without requiring extensive knowledge of statistics and machine learning. It simplifies the process of building high-performance machine learning applications, which traditionally required specialized data scientists and domain experts. Due to advancements in data science and AI, AutoML has seen significant progress in recent years.
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Healthcare & Lifesciences to account for higher CAGR during the forecast period
The AutoML market for healthcare is categorized into various applications, such as anomaly detection, disease diagnosis, drug discovery, chatbot and virtual assistance and others (clinical trial analysis and electronic health record (EHR) analysis). In the healthcare and life sciences industry, AutoML can help automate various tasks such as disease diagnosis, drug discovery, and patient care. AutoML can be used to analyze large volumes of medical data, such as electronic health records, medical images, and genomic data, to identify patterns and make predictions. This can help healthcare professionals make more accurate diagnoses, identify potential treatments, and improve patient outcomes. AutoML can also be used in drug discovery to identify potential drug candidates and optimize drug development processes. By analyzing molecular structures, genetic data, and other factors, AutoML can help identify potential drug targets and optimize drug efficacy and safety. AutoML can also be used to monitor patient progress and adjust treatment plans as needed. The implementation of AutoML in healthcare and life sciences should be done with caution and consideration for ethical and regulatory concerns.
Services Segment to account for higher CAGR during the forecast period
The market for Automated Machine Learning is bifurcated based on offering into solution and services. The CAGR of services is estimated to be highest during the forecast period. AutoML services allow users to automate various tasks involved in building and deploying machine learning models, such as feature engineering, hyperparameter tuning, model selection, and deployment. These services are designed to make it easier for businesses and individuals to leverage the power of machine learning without requiring extensive knowledge or expertise in the field.
Asia Pacific to exhibit the highest CAGR during the forecast period
The CAGR of Asia Pacific is estimated to be highest during the forecast period. Automated machine learning is rapidly growing in Asia Pacific, which includes China, India, Japan, South Korea, ASEAN, and ANZ (Australia and New Zealand). In recent years, there has been significant growth in the adoption of both AutoML and machine learning across various industries in Asia Pacific, driven by the region’s large and diverse datasets, as well as the need for faster and more efficient decision-making. Many companies in the region are also investing in the development of AutoML platforms and tools to help accelerate the adoption of AI and machine learning. To support the adoption of AutoML and machine learning, governments and organizations in the Asia Pacific region are investing in infrastructure and programs to promote innovation, education, and collaboration.
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Unique Features in the Automated Machine Learning (AutoML) Market
Because it enables people to comprehend the reasoning behind the model’s conclusions, this is an increasingly popular application of autoML. This is particularly significant for sectors like finance and healthcare, where adhering to regulations and having faith in the model’s judgment are essential.
Although time series data is common in many industries, it may not be well-suited for use with typical AutoML tools. Certain AutoML platforms provide features made especially for working with time series data, like algorithms made for time series forecasting and automatic feature engineering for time-based data.
Over time, alterations in real-world data might lead to a decline in a machine learning model’s performance. It can be useful to have an AutoML tool that can recognize data drift automatically and take remedial action, like retraining the model.
Algorithm bias and fairness are growing concerns as AI is used more and more. Platforms for autoML that provide tools to lessen prejudice and guarantee ethical and just AI development can set themselves apart.
Since autoML aims to democratize machine learning, even non-data scientists should find it simple to use. Citizens in the data science community may find AutoML easier to use with features like integrated visualizations, pre-built processes, and drag-and-drop interfaces.
Major Highlights of the Automated Machine Learning (AutoML) Market
The potential of AutoML to make machine learning accessible to a larger range of users is a major factor driving this rise. Employing AutoML enables companies to take use of AI capabilities even in the absence of a data science team by automating difficult processes such as model selection and hyperparameter tuning.
There is a far greater need for data scientists than there is skill in the field. This gap is filled by autoML, which enables non-expert citizen data scientists and analysts to create and implement machine learning models.
The time and resources needed to create and implement models are greatly decreased with autoML, which simplifies the ML development process. This enables companies to make data-driven decisions more effectively and to obtain insights from data more quickly.
With new businesses joining the market and incumbent ones growing their product lines, the AutoML market is experiencing ongoing innovation. More focus on certain use cases, such as time series data analysis, improved integration with current tools, and more user-friendly features are the results of this competition.
As AutoML becomes more widely used, guaranteeing responsible AI development is becoming more and more important. Differentiating features for AutoML platforms, such as bias detection and explainable AI (XAI), are becoming increasingly crucial.
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Top Companies in the Automated Machine Learning (AutoML) Market
Major vendors in the global Automated Machine Learning market are IBM (US), Oracle (US), Microsoft (US), ServiceNow (US), Google (US), Baidu (China), AWS (US), Alteryx (US), Salesforce (US), Altair (US), Teradata (US), H2O.ai (US), DataRobot (US), BigML (US), Databricks (US), Dataiku (France), Alibaba Cloud (China), Appier (Taiwan), Squark (US), Aible (US), Datafold (US), Boost.ai (Norway), Tazi.ai (US), Akkio (US), Valohai (Finland), dotData (US), Qlik (US), Mathworks (US), HPE (US), and SparkCognition (US).Microsoft
ServiceNow Inc. is known for providing enterprise cloud computing solutions. It delivers digital workflows on a single enterprise cloud platform called the Now Platform. The product portfolio of the firm is mainly focused on providing information technology and employee and customer workflows. ServiceNow offers solutions for IT operations management that covers service mapping, delivery, and assurance solutions; and business management such as financial management, project portfolio suite, vendor performance management, and performance analytics, including governance, risk, and compliance; and application development services. The company operates in North America, Europe, the Middle East, Africa, the Asia Pacific, and others. In recent years, ServiceNow has also made significant investments in the field of automated machine learning (AutoML). The company’s AutoML platform, called Now Intelligence, is designed to help businesses build and deploy machine learning models more efficiently. Now Intelligence offers a range of features, including data ingestion, data preparation, and model training and deployment. The platform is built on top of ServiceNow’s core platform, which means that customers can leverage their existing ServiceNow data and workflows to build machine learning models without having to learn new tools or languages. With the increasing demand for AI and machine learning solutions in various industries, ServiceNow’s Now Intelligence platform is positioned to be a significant player in the AutoML market.
Baidu is a leading Chinese technology company which was founded in 2000 and is headquartered in Beijing, China. It offers a range of internet-related services, including search engines, online advertising, cloud storage, and artificial intelligence (AI) solutions. It is one of the largest AI and internet companies, with a focus on developing cutting-edge technologies to improve people’s lives. It is operating through segments ranging from transaction services, iQIYI, and search services, the company has an array of vertical search-based products for end users and online marketing services for multinational companies, large domestic businesses, and SMEs. Baidu App, Baidu Search, Baidu Feed, Haokan, Baidu Post Bar, Baidu Knows, Baidu Encyclopedia, Baidu Maps, Baidu IME, popIn, Simeji, and Facemoji are the range of products offered for end users, while Pay for Placement (P4P) and non-P4P are online marketing services offered to customers. Baidu’s services cover a wide range of verticals, including healthcare, education, finance, transportation, and autonomous driving, among others. The company has a significant presence in China, with headquarters in Beijing and offices across the country, as well as international offices in the US, Japan, and other regions. In autoML, Baidu offers a platform called EZDL that allows users to create and train their own deep learning models without requiring extensive programming knowledge. EZDL uses a drag-and-drop interface and provides pre-built templates for various tasks, including image classification and object detection. It also offers automatic model tuning and optimization to improve model accuracy. Baidu’s autoML platform is designed to be accessible to a wide range of users, including small and medium-sized businesses.
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