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Machine learning market is projected to grow at a CAGR of 44.1% to hit USD 8.81 billion by 2022 illuminated by new report

Machine Learning Market is expected to grow from USD 1.41 billion in 2017 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1%.

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The proliferation of large and multidimensional data sets, rising focus towards solving real-time problems from data along with rising demand for sophisticated algorithm platform and tool is driving the adoption of machine learning across the globe.

The major issue in front of most of the organizations while incorporating machine learning in their business process is the lack of skilled employees including analytical talent, and the demand for those who can monitor analytical content is even greater.

Professional service segment is expected to have a larger market share during the forecast period

The service segment in the machine learning market includes professional and managed services. Majority of the companies do not have the expertise to successfully manage infrastructure, and hence, they outsource these services to third-party partners to maintain the level of security and safety.

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The growth of the professional services segment is mainly governed by the complexity of operations and increasing deployment of machine learning solutions.

 

Browse complete report on Machine Learning Market by Vertical (BFSI, Healthcare, and Life Sciences, Retail, Telecommunication, Government and Defense, Manufacturing, Energy, and Utilities), Deployment Mode, Service, Organization Size, and Region - Global Forecast to 2022 at www.rnrmarketresearch.com/machine-…arket-report.html

The major machine learning vendors are Microsoft Corporation (Washington, US), IBM Corporation (New York, US), SAP SE (Wall Dorf, Germany), SAS Institute Inc. (North Carolina, US), Google, Inc. (California, US), Amazon Web Services Inc. (Washington, US), Baidu, Inc. (Beijing, China), Big ML, Inc. (Oregon, US), Fair Isaac Corporation (FICO) (California, US), Hewlett Packard Enterprise Development LP (HPE) (California, US), Intel Corporation (California, US), KNIME.com AG (Zurich, Switzerland), RapidMiner, Inc. (Massachusetts, US),  Angoss Software Corporation (Toronto, Canada), H2O.ai (California, US), Alpine Data (California, US), Domino Data Lab, Inc. (California, US), Dataiku (Paris, France), Luminoso Technologies, Inc. (Massachusetts, US), Trademark Vision (Pennsylvania, US), Fractal Analytics Inc. (New Jersey, US),  TIBCO Software Inc. (California, US), Teradata (Ohio, US), Dell Inc. (Texas, US), and Oracle Corporation (California, US).

Large enterprises segment is expected to have a larger market size during the forecast period

The organization size segment in the machine learning market includes Small and Medium-Sized Enterprises (SMEs) and large enterprises. Emergence in the demand for cloud computing, cloud storage, IoT connected devices, and excessive use of smartphones are some of the prime reasons why large enterprises have turned toward machine learning for processing data.

In large enterprises, machine learning has a huge potential for the big data technology in allowing precise decision-making for superior performance.

Some major points from Table of Contents

1 Introduction
1.1 Objectives of the Study
1.2 Market Definition
1.3 Market Scope
1.4 Years Considered for the Study
1.5 Currency
1.6 Stakeholders

2 Research Methodology
2.1 Research Data
2.1.1 Secondary Data
2.1.2 Primary Data
2.1.2.1 Breakdown of Primaries
2.1.2.2 Key Industry Insights
2.2 Market Size Estimation
2.2.1 Bottom-Up Approach
2.2.2 Top-Down Approach
2.3 Microquadrant Research Methodology
2.3.1 Vendor Inclusion Criteria
2.4 Research Assumptions
2.5 Limitations

 

4 Premium Insights
4.1 Attractive Market Opportunities in the Machine Learning Market
4.2 Machine Learning Market: Top 3 Verticals
4.3 Lifecycle Analysis, By Region, 2017–2022

 

5 Market Overview and Industry Trends
5.1 Introduction
5.2 Market Dynamics
5.2.1 Drivers
5.2.1.1 Technological Advancements
5.2.1.2 Proliferation in Data Generation
5.2.2 Restraints
Trends

Asia Pacific (APAC) is expected to witness the highest growth rate during the forecast period

APAC is estimated to grow at the highest CAGR during the forecast period. Factors, such as continual growth in the mobile network, increasing the complexity of business, rise in demand for intelligent business processes, and exponential growth in data generation throughout the industry verticals are driving the machine learning market in the APAC region.

The North American region is expected to have the largest market share during the forecast period. The major growth drivers for this region are the large-scale investments in implementing machine learning services due to the growth in demand for processed data.

Moreover, recently the region also witnessed the widespread adoption of cloud-based machine learning platform among large enterprises and SMEs across multiple verticals.

In the process of determining and verifying the market size for several segments and subsegments gathered through secondary research, extensive primary interviews were conducted with the key people.

  1. By Company Type - Tier 1 - 18%, Tier 2 - 48%, and Tier 3 - 34%
  2. By Designation – C-level -22%, Director-level -43%, and Others-35%
  3. By Region –North America -42%, EMEA (Europe, Middle East and Africa) -32%, and APAC (AsiaPacific)- 26%

 

Research Coverage

The machine learning market has been segmented on the basis of verticals, deployment modes, organization sizes, services, and region. The machine learning is segmented on the basis of verticals into Banking, Financial Services, and Insurance (BFSI), energy and utilities, healthcare and life sciences, retail, telecommunications, manufacturing, government and defense, and others (transportation, agriculture, media and entertainment, and education).

The verticals are further segmented on the basis of application areas, applications of machine learning in BFSI includes fraud and risk management, investment prediction, sales and marketing campaign management, customer segmentation, digital assistants, and others (compliance management and credit underwriting).

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 Applications of machine learning in healthcare and life sciences include disease identification and diagnosis, image analytics, drug discovery/manufacturing, personalized treatment, and others (clinical trial research and epidemic outbreak prediction). Applications of the machine learning in retail include inventory planning, up-sell and cross-channel marketing, segmentation and targeting, recommendation engines, and others (customer ROI and lifetime value and customization management).

Applications of machine learning in telecommunication include customer analytics, network optimization, network security, and others (digital assistance/contact centers analytics and marketing campaign analytics).

Applications of machine learning in government and defense include threat intelligence, autonomous defense system, and others (sustainability and operational analytics). Applications of machine learning in manufacturing include predictive maintenance, demand forecasting, revenue estimation, supply chain management, and others (root cause analysis and telematics).

Applications of machine learning in energy and utilities include power/energy usage analytics, seismic data processing, smart grid management, carbon emission, and others (customer specific pricing and renewable energy management).

The services offered in the machine learning market include professional and managed services. The deployment modes in the machine learning market include the cloud and on-premises.

The organization sizes are segmented into Small and Medium-Sized Enterprises (SMEs) and large enterprises. Finally, on the basis of regions, the machine learning market is segmented into North America, Europe, APAC, the Middle East and Africa (MEA), and Latin America.

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