Press release
Automated Machine Learning Market (AutoML) to Grow USD 6.4 billion by 2028 | Key Players, Value, Demands, Application
The market for Automated Machine Learning is projected 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. Explainable AI is a crucial aspect of AutoML that aims to provide transparency into how machine learning models make predictions. By using explainable AI techniques, such as feature importance and decision trees, businesses can gain insights into how their models work and make more informed decisions.Get a FREE Sample Report at https://www.reportsnreports.com/contacts/requestsample.aspx?name=7104179
Major vendors offering Automted Machine Learning solutions and services across the globe 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).
The automated machine learning (AutoML) market refers to the industry involved in the development and deployment of software tools and platforms that automate various stages of the machine learning process. AutoML aims to simplify and streamline the process of building and deploying machine learning models, making it accessible to users with limited data science expertise.
Machine learning involves complex tasks such as data preprocessing, feature engineering, model selection, hyperparameter tuning, and model deployment. AutoML solutions automate these tasks, allowing users to build machine learning models without extensive knowledge of algorithms or programming.
The AutoML market has experienced significant growth in recent years due to several factors. First, the increasing adoption of machine learning across various industries has created a demand for simplified and efficient model development processes. AutoML enables businesses to leverage the power of machine learning without relying heavily on data scientists.
Second, the shortage of skilled data scientists and machine learning experts has driven the need for automated solutions that democratize machine learning capabilities. AutoML tools enable non-experts, such as business analysts or domain experts, to create and deploy machine learning models, thereby accelerating the adoption of AI-driven solutions.
Key components of the AutoML market include software tools, platforms, and cloud-based services. These offerings typically provide functionalities such as data preparation, automated feature engineering, algorithm selection, hyperparameter optimization, and model deployment. Some AutoML solutions also incorporate advanced techniques like neural architecture search and automated deep learning.
The AutoML market is highly competitive, with both established software vendors and startups offering a range of solutions. Major players include technology companies, cloud service providers, and specialized AutoML platform providers.
The market is also influenced by trends such as interpretability and explainability of machine learning models, ethical considerations in AI, and the integration of AutoML into existing data science workflows. As organizations become more concerned about model transparency, fairness, and compliance, AutoML solutions are adapting to address these requirements.
Geographically, the AutoML market is expanding globally, with adoption across various industries such as healthcare, finance, retail, manufacturing, and transportation. Different regions are witnessing different levels of adoption, influenced by factors like technological maturity, industry landscape, and regulatory environment.
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Overall, the automated machine learning market is expected to continue growing as organizations increasingly recognize the value of machine learning and seek user-friendly solutions to leverage its benefits. The market will likely witness advancements in automation techniques, integration with AI ethics and governance frameworks, and collaboration with domain experts to further simplify and democratize machine learning processes.
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