AutoML,
or 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. The AutoML market has been expanding rapidly
in recent years, driven by the increasing demand for machine learning solutions
across a variety of industries. AutoML tools offer a range of functionalities,
such as automating feature engineering, hyperparameter tuning, model selection,
and deployment. This allows data scientists, engineers, and businesses to build
and deploy high-quality machine learning models much faster and with less expertise
required.
Automated
Machine Learning (AutoML) Market size 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, according to report published by MarketsandMarkets.
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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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Market
Players
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).
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