MACHINE LEARNING AS A SERVICE MARKET ANALYSIS

1. Coherent Market InsightsMarket Research and Business Consulting Serviceshttps://www.coherentmarketinsights.comMACHINE LEARNING AS A SERVICE MARKET ANALYSIS

2. Market Overview Machine learning as-a-service refers to various types of services that offer machine learning abilities as a part of cloud-computing services. MLaaS assists its customers to gain advantage from machine learning without additional cost, risk, and time for creating an in-house internal machine learning team. MLaaS offers various advantages such as data pre-processing, model evaluation, model training, and predications. Many service providers offer API, predictive analytics, deep learning, data visualization, natural language processing, etc. The major advantage that MLaaS offers to businesses is that it enables them to get started quickly with machine learning without undergoing tedious software installation processes. Companies can enhance their product capabilities and offerings, enhance regular business operation efficiency, make interaction with consumers easier, and use AI prediction capabilities. The global machine learning as a service (MLaaS) market was valued US$ 2,802.8Mn in 2019 and expected to grow at a CAGR of 36.8% over the forecasted period 2019-27.

3. Market Dynamics- Drivers Exponential growth of big data is expected to drive growth of the global machine learning as a service (MLaaS) market during the forecast period Growing internet penetration has led to increase in a massive amount of structured and unstructured data in organizations. It has become extremely crucial for these organizations to get a better insight into their data, in order to enhance efficiency and competitiveness. Moreover, many organizations are increasingly adopting machine learning as a service to analyze both structured and unstructured data for future predictions and also use it for further marketing purposes. This, in turn, is expected to drive growth of the global machine learning as a service (MLaaS) market during the forecast period. Rising acceptance of cloud-based technologies is expected to propel the global machine learning as a service (MLaaS) market growth over the forecast period According to Cisco’s Global Cloud Index, 2015-2020, 65% of data traffic in 2015 was cloud-based, which is estimated to reach 92% by 2020. As more and more companies are preferring cloud computing, it has become easy for them to adopt machine learning services. Moreover, machine learning application on the cloud has become more accessible and economical for businesses. Thus, these factors are expected to boost the global machine learning as a service (MLaaS) market growth in the near future.

4. North America region dominated the global machine learning as a service (MLaaS) market in 2019, accounting for 32.8% share in terms of value, followed by Europe, Asia Pacific, Middle East and Africa, and South America respectively.

5. Market Dynamics- Restraints Low availability of skilled personnel is expected to hamper the global machine learning as a service (MLaaS) market growth during the forecast period Low availability of data scientists with machine learning experience is a major threat to growth of the market. Though machine learning software saves data scientists’ effort, a professional is required to use data and create the algorithms required for machine learning. Thus, a lack of skilled and experienced workforce combined with limited knowledge among end users is expected to hamper the global machine learning as a service (MLaaS) market growth in the near future. Lack of data security is expected to hinder the global machine learning as a service (MLaaS) market growth over the forecast period A number of organizations are unwilling to adopt machine learning technologies due to concerns regarding data security. For many regulated industry sectors such as banking, insurance, healthcare, and government, security of data is important and any failure or gap in data security may result in major problems. Hence, these factors are expected to hinder the global machine learning as a service (MLaaS) market growth over the forecast period.

6. Market Opportunities IoT-enabled services can provide major growth opportunities to market players Internet of Things (IoT) refers to connecting any device with the help of internet, allowing them to communicate with each other. To make this feasible, several sensors are installed on different devices to generate data. However, there is also need to make sense out of data gathered from the interconnection of devices. Since the massive amount of data is generated from various sensors connected to devices, the analysis of data is one of the biggest challenges associated with IoT. As the amount of data is expected to grow bigger in size in the future, enabling IoT will get more difficult. With the help of machine learning as a service, organizations can overcome this challenge. Machine learning can take an enormous amount of data, analyze it and generate patterns out of it without any specific programing. Rising investments in healthcare industry can offer significant opportunities in the near future Healthcare sector offers massive growth opportunities for MLaaS providers, due to a vast amount of data generation. In any healthcare institution, massive data is generated including patient name, medical history, disease name, treatment process, treatment period, etc. All this is unstructured data, which will require machine learning capabilities to analyze and processes it.

7. Market Trends Advent of new intelligent application is expected to be major trend Machine learning capabilities are expected to be integrated into more platforms and software in the years to come, enabling organizations to take advantage of them. A number of companies are focused on becoming a data company irrespective of what an organization does. Previously, organizations have been dependent on structured data to make appropriate decisions or estimate future outcomes. However, upsurge of big data and machine learning capabilities has allowed analysis of unstructured data to make more informed decisions. Moreover, rapid speed of data generation and the availability of a huge amount of computing power are expected to facilitate advent of more and more applications that generate real-time predictions and get better constantly over time. Growing use of machine learning in retail sector is another major trend Major retailers in the market have collaborated with analytics companies to gain insights for marketing purposes. Moreover, smaller retailers are leveraging power of big data to understand its consumers, owing to availability of cost-efficient cloud-based machine learning solutions. In segment-of-one marketing, the individual customers are targeted with the available data about them to suggest the right promotion for the right customer. Machine learning-powered marketing is expected to change the traditional method of promotion.

8. In global machine learning as a service (MLaaS) market, by Type segment, the Private Cloud/VPCs, sub-segment dominated the global market in 2019, accounting for 50.2% share in terms of value, followed by Public Cloud. Segment information

9. Competitive Section Key players involved in the global machine learning as a service (MLaaS) market are H2O.ai, Google Inc., Predictron Labs Ltd, IBM Corporation, Ersatz Labs Inc., Microsoft Corporation, Yottamine Analytics, Amazon Web Services Inc., FICO, and BigML Inc.

10. Key Developments Key companies in the market are focused on product launches, in order to expand the product portfolio. For instance, in February 2017, IBM Corporation intruded the Machine Learning Cognitive Platform for the private cloud, which is to be used with the IBM z System Mainframe. Major market players are involved in partnerships and collaborations, in order to enhance the market presence. For instance, in Mach 2017, Microsoft Corporation partnered with Ecolab and Trucost to launch the Water Risk Monetizer tool. The Water Risk Monetizer is the first publicly available financial modeling tool enabling businesses to factor current and future water risks into decision makin

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