Published: February 2020 | Report Code: IM11843 | Available Format: PDF | Pages: 155
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). Increasing demand for efficient fraud detection solutions, growing need for personalized product recommendation, and rising importance of predictive lead scoring are some of the key factors driving the growth of the market across the globe. APAC is expected to record the fastest growth 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 and deployment of artificial intelligence (AI) technology.
Factors Governing Automated Machine Learning Market
Increasing preferences toward cloud-based platform is observed as a major trend in the market. Cloud-based platforms involve Software-as-a-Service (SaaS) model, which enables users to access these solutions virtually, through a secured platform over the internet. Cloud deployment offers greater flexibility and scalability, and reduces associated IT infrastructure costs.
With rising number of fraudulent incidents, detection and prevention of fraud is a huge challenge for organizations across all verticals. Presently, a large number of companies are using big data and traditional machine learning methods to detect and prevent fraud. However, as the dataset tends to be imbalanced and highly skewed in terms of positive and negative classes, to get accurate results, typically involving high domain knowledge and a large number of processes are required for data preprocessing, data exploration, missing data handling, training, feature engineering, model selection, evaluation, and so forth. Auto Machine Learning solutions optimize all these processes without human intervention, which is further driving the growth of the market.
The expanding healthcare industry is expected to offer immense growth opportunities for the market players worldwide. Presently, the healthcare industry across the globe is facing several challenges with cost, patient outcomes, and medical reporting errors. Thus, these institutes are investing heavily in new technologies to improve their operations and increase revenue. IT can help healthcare institutes to maximize revenue, improve patient outcomes, and improve overall clinical workflow.
Automated Machine Learning Market Segmentation Analysis
The platform category under the offering segment generated the larger revenue in the automated machine learning market in 2019. This is due to the increasing adoption of AutoML platforms across all verticals for fraud minimization, operational costs reduction, and enhanced customer service.
The cloud deployment category is expected to record the faster growth during the forecast period in the market. This can be attributed to the enhanced scalability and flexibility of cloud platforms, where clients can easily customize solutions and services as per their requirements.
The large enterprise category under the enterprise size segment held the larger share in the market in 2019. With dispersed operations across the globe, large enterprises are increasingly adopting AutoML solutions for cost reduction, competitor analysis, customer retention, and effective, data-driven decision-making strategies.
The sales & marketing management category under the application segment is expected to witness fastest growth in the automated machine learning market during the forecast period. Several companies across all verticals significantly use AutoML platforms to gain insights into customer emotion, and further facilitate content personalization, lead scoring, customer segmentation, and customer engagement.
|Market Size by Segments||Offering, Deployment Type, Enterprise Size, Application, Industry|
|Market Size of Geographies||U.S., Canada, Germany, U.K., France, Italy, Belgium, China, Japan, India, South Korea, Brazil, Mexico|
|Market Players||U.S., Canada, U.K., Germany, France, Italy, Spain, China, Japan, Australia, South Korea, India, Turkey, U.A.E., Saudi Arabia, Egypt, South Africa, Brazil, Mexico|
Geographical Analysis of Automated Machine Learning Market
In the global automated machine learning market, North America recorded the largest revenue in 2019. Technological advancement; developed IT infrastructure; increasing presence of market players; and advanced IT & telecom, healthcare, and banking, financial service, & insurance (BFSI) industries are the major factors driving the growth of the market in the region.
Competitive Landscape of Automated Machine Learning Market
The automated machine learning market is highly competitive in nature, due to the presence of a large number of players. Some of the prominent market players include DataRobot Inc., H2O.ai Inc., dotData Inc., EdgeVerve Systems Limited, Amazon Web Services Inc., Squark, Big Squid Inc., SAS Institute Inc., Microsoft Corporation, Google LLC, Determined AI, and Aible Inc.
Recent Strategic Developments of Major Automated Machine Learning Market Players
In recent years, major players in the automated machine learning market have taken several strategic measures to ensure their foothold. For instance, in June 2019, DataRobot Inc. acquired ParallelM Inc., a U.S.-based fully dedicated Machine Learning Operations (MLOps) company, for an undisclosed amount. With this acquisition, the former aims to expand the current model monitoring and management capabilities of its platform.
Furthermore, in May 2019, Big Squid Inc. partnered with Snowflake Inc., a data warehouse solution and service provider, to integrate Snowflake’s data warehouse solutions with its Kraken AutoML platform. This integration would help Kraken users to build predictive analytics models in very less steps.
Market Size Breakdown by Segment
The AutoML market report offers comprehensive market segmentation analysis along with market estimation/estimates for the period 2014–2030.
Based on Offering
Based on Deployment Type
Based on Enterprise Size
Based on Application
Based on Industry
Key Questions Addressed/Answered in the Report
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