Automated Machine Learning (AutoML)

Automated Machine Learning (AutoML)

Global Automated Machine Learning (AutoML) Market to Reach US$10.9 Billion by 2030

The global market for Automated Machine Learning (AutoML) estimated at US$1.1 Billion in the year 2023, is expected to reach US$10.9 Billion by 2030, growing at a CAGR of 39.3% over the analysis period 2023-2030. AutoML Solutions, one of the segments analyzed in the report, is expected to record a 35.3% CAGR and reach US$5.6 Billion by the end of the analysis period. Growth in the AutoML Services segment is estimated at 44.8% CAGR over the analysis period.

The U.S. Market is Estimated at US$291.9 Million While China is Forecast to Grow at 36.7% CAGR

The Automated Machine Learning (AutoML) market in the U.S. is estimated at US$291.9 Million in the year 2023. China, the world`s second largest economy, is forecast to reach a projected market size of US$1.5 Billion by the year 2030 trailing a CAGR of 36.7% over the analysis period 2023-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 34.5% and 33.5% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 25.9% CAGR.

Global Automated Machine Learning (AutoML) Market - Key Trends and Drivers Summarized

Automated Machine Learning (AutoML) is emerging as a transformative force in the field of artificial intelligence, designed to automate and streamline the often complex and time-consuming tasks of developing machine learning models. The key appeal of AutoML lies in its ability to make machine learning more accessible to non-experts and to enhance the efficiency of model development, making it a critical tool as industries increasingly seek to leverage AI capabilities. By automating the labor-intensive processes of data preprocessing, model selection, and parameter tuning, AutoML enables a more rapid deployment of machine learning models. This not only democratizes AI by reducing the need for specialized knowledge but also significantly expedites the AI development cycle, allowing businesses to quickly adapt to market changes and new data.

The rapid adoption of AutoML is driven by several compelling factors, chief among them the growing complexity of machine learning models and the pressing shortage of skilled data scientists. As machine learning applications become more sophisticated, the expertise required to effectively develop and tune these models escalates. AutoML addresses this challenge by simplifying critical tasks such as feature and algorithm selection and hyperparameter tuning, substantially lowering the barrier to advanced machine learning for organizations without deep technical resources. Additionally, the shortage of data scientists has catalyzed the need for tools that empower users with minimal technical background to undertake tasks traditionally reserved for experts. This democratization is crucial for organizations striving to initiate or accelerate their AI strategies in a competitive business environment.

Moreover, the integration of AutoML with advancements in AI and computing power, along with its synergy with cloud computing platforms, is expanding its application across various industries. This integration provides scalable computing resources essential for running complex models and supports the burgeoning demand for predictive analytics in sectors like healthcare, finance, and retail. Despite these advantages, the deployment of AutoML brings challenges, including the need for ongoing oversight by experienced practitioners to ensure that models are applied correctly and ethically. Concerns around data privacy, potential biases in decision-making, and the overall transparency of AI systems also pose significant hurdles. As AutoML continues to evolve, addressing these ethical and practical challenges will be paramount to fully realizing its potential and ensuring its responsible use across industries.

Select Competitors (Total 23 Featured) -

• Alteryx, Inc.
• Amazon Web Services, Inc.
• Databricks, Inc.
• Dataiku SAS
• DataRobot, Inc.
• dotData
• Google Cloud Platform
• IBM Corporation
• Microsoft Learn
• The MathWorks, Inc.

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