Asia Pacific leads in rapid adoption of Automated Machine Learning

Rising acceptance, technological breakthroughs, integration with other developing technologies, and an emphasis on customization and personalization are driving the bright future of the automated machine learning (AutoML) market, which has the power to completely transform the machine learning industry.

According to a new report by MarketsandMarkets, the AutoML market will likely grow from $1 billion in 2023 to $6.4 billion by 2028, at a CAGR of 44.6% during the forecast period.

AutoML is a rapidly growing field that automates many of the time-consuming and complex tasks involved in building and deploying machine learning models. This allows businesses and individuals to leverage the power of machine learning without requiring extensive knowledge or expertise in the field.

AutoML tools offer a range of functionalities, such as automating feature engineering, hyperparameter tuning, model selection, and deployment, enabling data scientists, engineers, and businesses to build and deploy high-quality machine learning models faster.

Highest CAGR expected in Asia Pacific during the forecast period

During the forecast period, the CAGR of Asia Pacific is expected to be the highest. The region, which comprises China, India, Japan, South Korea, ASEAN, and ANZ (Australia and New Zealand), is experiencing rapid growth in automated machine learning and machine learning adoption across various industries. This growth is driven by the region’s large and diverse datasets, as well as the need for faster and more efficient decision-making.

Additionally, many companies in the region are investing in the development of AutoML platforms and tools to accelerate the adoption of AI and machine learning. To promote innovation, education, and collaboration, governments and organizations in the Asia Pacific region are also investing in infrastructure and programs to support the adoption of AutoML and machine learning.

Automated Machine Learning in the services segment to have exponential growth

The market for Automated Machine Learning, divided into solution and service offerings, is most likely to have the highest CAGR in the services segment during the forecast period.

AutoML services automate several tasks related to developing and deploying machine learning models, such as feature engineering, hyperparameter tuning, model selection, and deployment. These services aim to simplify the process of utilizing machine learning for businesses and individuals without requiring a vast amount of knowledge or expertise in the field.

Advantages of AutoML

• AutoML greatly decreases the time and effort required to create a machine learning model by automating numerous time-consuming processes, including feature engineering, hyperparameter tuning, model selection, and deployment, freeing up developers to work on more crucial projects.
• AutoML lowers the expenses involved with creating machine learning models by removing the requirement for specialized knowledge and skills, and it lessens the requirement for substantial computing resources, which are costly to acquire and maintain.
• AutoML democratizes the discipline and opens it up to more users, making it possible for anyone with little experience in machine learning to develop and utilize machine learning models.
• AutoML can expand to handle enormous datasets and difficult jobs, allowing organizations to analyze more data and gain insights more quickly.
• AutoML helps lessen the risk of biases and errors in machine learning models that can be brought about by human error or arbitrary judgment.

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:

• Slow adoption of Machine learning tools
• Lack of standardization and regulations

Opportunities:

• Capitalizing on the 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

WRITTEN BY

Team Eela

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