Automated Machine Learning (AutoML) Market
The market for automated machine learning is expected to grow at a CAGR of 44.6% during the forecast period, from USD 1.0 billion in 2023 to USD 6.4 billion by 2028. AutoML (automated machine learning) is a rapidly growing field that aims to automate many of the time-consuming and complex tasks involved in building and deploying machine learning models.
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The AutoML market has expanded rapidly in recent years, driven by the growing demand for machine learning solutions across industries. AutoML tools offer a range of capabilities, including feature engineering, hyperparameter tuning, model selection, and automated deployment, enabling data scientists, engineers, and enterprises to build and deploy high-quality machine learning models faster, without the need for specialized knowledge.
Healthcare and life sciences to account for the highest CAGR during the forecast period
The AutoML for Healthcare market is categorized into various applications such as anomaly detection, disease diagnosis, drug discovery, chatbots, virtual assistants, and others (clinical trial analytics, electronic health record (EHR) analytics). In the healthcare and life sciences industry, AutoML helps 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 helps 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 the drug development process. By analyzing molecular structures, genetic data, and other factors, AutoML helps identify potential drug targets and optimize drug efficacy and safety. AutoML can also be used to monitor patient progress and adjust treatment plans if necessary. The implementation of AutoML in healthcare and life sciences should be done carefully, taking into account ethical and regulatory concerns.
Services sector to account for higher CAGR during forecast period
The automated machine learning market is bifurcated based on solution and services offering. Services is projected to grow at the highest CAGR during the forecast period. AutoML services enable users to automate various tasks associated with building and deploying machine learning models, including 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 domain.
Asia Pacific to exhibit highest CAGR during forecast period
Asia Pacific is projected to witness the highest CAGR during the forecast period. Automated machine learning is growing rapidly in Asia Pacific, including China, India, Japan, Korea, ASEAN, and ANZ (Australia and New Zealand). In recent years, large and diverse datasets in Asia Pacific and the need for faster and more efficient decision-making have led to a significant increase in the adoption of both AutoML and machine learning across various industries. Many companies in the region are also investing in the development of AutoML platforms and tools to accelerate their adoption of AI and machine learning. To support the adoption of AutoML and machine learning, governments and organizations in Asia Pacific are investing in infrastructure and programs that foster innovation, education, and collaboration.
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Top automated machine learning companies:
Key vendors in the global automatic 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).
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