Automated Machine Learning (AutoML) Market worth $6.4 billion by 2028 - Exclusive Report by MarketsandMarkets™

CHICAGO, May 12, 2023 /PRNewswire/ -- The market for automated machine learning (AutoML) appears to have a bright future thanks to rising acceptance, technological breakthroughs, integration with other developing technologies, and an emphasis on customisation and personalization. It is anticipated to improve in accessibility, scalability, and ethics and has the power to completely alter the machine learning industry.

The Automated Machine Learning Market 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 a new report by MarketsandMarkets™. 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.

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257 - Tables
51 - Figures
316 - Pages

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Scope of the Report

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 analyse 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 analysing 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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Automated Machine Learning (AutoML) Market Dynamics

Driver:

• Growing demand for improved customer satisfaction and personalized product recommendations through AutoML
• Increasing need for accurate fraud detection
• Growing data volume and complexity
• Rising need to transform businesses with Intelligent automation using AutoML

Restraint:

• Machine learning tools are being slowly adopted
• Lack of standardization and regulations

Opportunities:

• Capitalizing on growing demand for AI-enabled solutions
• Integration with complementary technologies
• Seizing opportunities for faster decision-making and cost savings

Challenges:

• Increasing shortage of skilled talent
• Difficulty in Interpreting and explaining AutoML models
• Data privacy in AutoML

Top Key Companies in 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).

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.

Automated Machine Learning (AutoML) Market Advantages

• The time and effort needed to create a machine learning model can be greatly decreased by using AutoML. By automating numerous time-consuming processes including feature engineering, hyperparameter tuning, model selection, and deployment, it frees up developers to work on more crucial projects.
• By removing the requirement for specialised knowledge and skills, AutoML lowers the expenses involved with creating machine learning models. Additionally, it lessens the requirement for substantial computing resources, which are costly to acquire and maintain.
• AutoML makes it possible for anyone with little experience in machine learning to develop and utilise machine learning models, democratising the discipline and opening it up to more users.
• Organisations can analyse more data and gain insights more quickly because to the ease with which AutoML can expand to handle enormous datasets and difficult jobs.
• Machine learning models are susceptible to biases and errors that can be brought about by human error or arbitrary judgement. AutoML helps lessen this risk.

Report Objectives

• To define, describe, and forecast the Automated Machine Learning Market based on offering, application, vertical, and region
• To provide detailed information about the major factors (drivers, restraints, opportunities, and challenges) influencing the market growth
• To analyze subsegments with respect to individual growth trends, prospects, and contributions to the total market
• To analyze opportunities in the market for stakeholders and provide the competitive landscape of the market
• To forecast the revenue of the market segments with respect to all the five major regions, namely, North America, Europe, Asia Pacific (APAC), the Middle East & Africa (MEA), and Latin America
• To profile the key players and comprehensively analyze the recent developments and their positioning related to the Automated Machine Learning Market
• To analyze competitive developments, such as mergers & acquisitions, product developments, and research & development (R&D) activities, in the market
• To analyze the impact of recession across all the regions across the Automated Machine Learning Market

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