The global automated machine learning (AutoML) market generated the revenue of $269.6 million in 2019, and is expected to reach $14,511.9 million by 2030, advancing at a CAGR of 43.7% during the forecast period (2020–2030). The cloud category under the deployment type segment is expected to record the fastest growth during the forecast period. This can be ascribed to the enhanced scalability and flexibility offered by cloud-based platform, where clients can customize solutions and services as per their requirements.
Growing Need for Personalized Product Recommendation Is Escalating the Market Growth
With the increasing popularity of online shopping, demand for personalized content is increasing, owing to customers preference for products to meet their specific demand. Personalized product recommendations help companies to increase average order value. Thus, to meet the evolving needs of customers for updated products, companies are investing heavily in new technologies to offer best product recommendations. AutoML solutions find patterns in customer behavior from clickstream data, prior purchases, demographics, browsing history, and previous product searches, and create 1:1 personalized product recommendation list that matches consumer needs and preferences.
Rising Importance of Effective Product Assortment Is Offering Immense Growth opportunities to the Market Players
The rising importance of effective product assortment in retail store network is expected to generate immense opportunities in the automated machine learning market. Choosing the right mix of products in a retail store is very important for retailers in order to meet the needs of customers and retain customer base. AutoML solutions can be ideal for retailers for effective product assortment. The solution can look at various factors, such as location, customer segments, weather patterns, store display space, and past sales records, to find out which products would be the best fit for a given store location.
Segmentation Analysis of Automated Machine Learning Market
Geographical Analysis of Automated Machine Learning Market
Together, North America and Europe are expected to hold over 65% share cumulatively in the automated machine learning market in 2030. All the major investments are being recorded in the U.S., Canada, Germany, U.K., and France. Further, technological advancement, developed IT infrastructure, and increasing adoption of emerging technologies are some of the key factors driving the growth of the market in the regions.
APAC is expected to register fastest growth in the market during the forecast period. This can be attributed to the rising economic growth, increasing investment in IT infrastructure, significant adoption of emerging technologies, and increasing government initiatives toward the development of artificial intelligence (AI) technology.
Browse report overview with detailed TOC on "Automated Machine Learning (AutoML) Market Research Report: By Offering (Platform, Service), Deployment Type (On-Premises, Cloud), Enterprise Size (Large Enterprise, Small & Medium Enterprise), Application (Fraud Detection, Sales & Marketing Management, Medical Testing, Transport Optimization), Industry (BFSI, IT & Telecom, Healthcare, Government, Retail, Manufacturing) - Industry Size, Share, Development and Demand Forecast to 2030" at:https://www.psmarketresearch.com/market-analysis/automated-machine-learning-market
Competitive Landscape of Automated Machine Learning Market
The automated machine learning market is highly competitive with large number of key players, including DataRobot Inc., H2O.ai Inc., dotData Inc., and EdgeVerve Systems Limited, Amazon Web Services Inc., Squark, Big Squid Inc., SAS Institute Inc., Microsoft Corporation, Google LLC, Determined AI, and Aible Inc.
In recent years, major players in the market have taken several strategic measures to strengthen their positions. For instance, in August 2019, Aible Inc. launched Aible Advanced, a fully automated machine learning AI platform for data scientists and developers. The platform automates all repetitive parts of machine learning processes and helps to generate best predictive models in less time. The new platform would enable data scientists to achieve business impact quickly and more efficiently.
