Automated Machine Learning Market Recent Trends, Outlook, Size, Share, Top Companies, Industry Analysis, Future Development & Forecast - 2028

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 Automated Machine Learning (AutoML) market [https://www.marketsandmarkets.com/Market-Reports/automated-machine-learning-market-193686230.html?utm_campaign=automatedmachinelearningmarket&utm_source=abnewswire.com&utm_medium=paidpr] is projected to grow from USD 1.0 billion in 2023 to USD 6.4 billion by 2028, at an impressive CAGR of 44.6% over the forecast period. AutoML, a subset of artificial intelligence (AI), empowers users to develop machine learning applications without needing in-depth expertise in statistics or machine learning. This technology streamlines the creation of high-performance machine learning models, a process that traditionally required the skills of specialized data scientists and domain experts. Recent advancements in data science and AI have driven significant progress in AutoML, enhancing its capabilities and accessibility.

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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 Market

Automated Machine Learning (AutoML) platforms are designed to make machine learning accessible to users without requiring deep expertise in data science. By automating complex processes like data preprocessing, model selection, and hyperparameter tuning, AutoML enables non-experts to create effective machine learning models with minimal manual intervention.

AutoML significantly reduces the time and resources required for developing machine learning models. Traditionally, creating high-performance models involved lengthy processes managed by data scientists and engineers. AutoML streamlines these steps, accelerating model development and lowering costs.

AutoML platforms are highly scalable, allowing companies to adjust their machine learning workflows based on the volume of data and complexity of tasks. This scalability makes AutoML suitable for a range of industries, from healthcare to finance and retail, where companies can adapt these tools to suit their specific needs.

AutoML employs advanced optimization techniques, including hyperparameter tuning, feature engineering, and model ensembling, to enhance model performance. These techniques help users achieve high accuracy and efficiency without needing to manually configure these settings, producing models that rival those crafted by experienced data scientists.

A defining feature of the AutoML market is its role in democratizing access to AI and data science. By lowering the skill barrier, AutoML enables more people within organizations to work with machine learning, leading to increased innovation and collaboration.

Major Highlights of the Automated Machine Learning Market

AutoML significantly lowers the barriers to entry for machine learning by enabling users without extensive data science expertise to build robust models. This democratization allows organizations across industries to leverage AI insights, empowering departments such as marketing, finance, and operations to innovate and make data-driven decisions without depending solely on data science teams.

AutoML offers considerable time and cost savings by automating the traditionally labor-intensive steps involved in machine learning, such as data preprocessing, feature selection, and hyperparameter tuning.

The AutoML market is seeing widespread adoption across diverse sectors, including healthcare, finance, retail, and manufacturing. This broad applicability highlights AutoML's versatility, with organizations using it for applications ranging from predictive maintenance in manufacturing to personalized recommendations in retail.

AutoML platforms are increasingly integrated with major cloud services and existing data pipelines, allowing businesses to seamlessly incorporate AutoML into their data infrastructures. With compatibility across platforms such as AWS, Google Cloud, and Microsoft Azure, AutoML enhances data accessibility and enables companies to deploy models efficiently in cloud or hybrid environments.

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Top Companies in the Automated Machine Learning 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).

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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