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An uptick in the adoption of IoT (Internet of Things)-based applications is generating huge demand for machine learning (ML) to ensure the accuracy of business operations using IoT platforms. Thus, integration of the IoT technology within different industry verticals helps to provide better customer experiences. This factor is estimated to boost the growth of the machine learning as a service market. The growing demand for adoption of artificial intelligence (AI)-based solutions among small & medium enterprises (SMEs) to improve efficiency and productivity is expected to propel the machine learning as a service market.
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The healthcare industry is opening up new opportunities for stakeholders in the machine learning as a service market during the ongoing COVID-19 fallout. Apart from the growing adoption of cloud-based technologies by small & medium businesses (SMBs), the desire to understand each individual independently and serve accordingly is further augmenting growth in the global machine learning as a service market.
With an ability to bifurcate different clientele, vendors are able to provide personalized services and develop a brand name for themselves. The market for MLaaS (Machine Learning as a Service) is also expected to gain traction from emerging application possibilities with technological advancements, increased investment across the healthcare industry, and increase in data from IoT platforms.
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The growing adoption of cloud technologies using desirable delivery models in different industry verticals helps developers to provide efficient cloud-based solutions to handle business operations. As more SMBs in the machine learning as a service market implement cloud-based services, the time-consuming process of machine learning integration will become much less time-consuming. By eliminating repetitive tasks, it helps to boost an enterprise’s productivity without hiring human resource.
However, lack of skilled consultants, compliance issues, and governmental regulations are some of the restraints curtailing the progress of this market. Hence, market stakeholders should collaborate with government and regulatory authorities to improve standardization in the market landscape.
MLaaS offers various services for predictive analytics, data visualization, and advanced machine learning algorithms to developers, along with adoption of advance technologies such as cloud and AI in different industry verticals. This helps to deliver machine learning applications to the end users. Such trends are expected to drive the adoption of machine learning as a service throughout the assessment period.
Machine learning as a service is a set of cloud tools that help to modify the daily work data using algorithms. It helps developers to save time, cost, and risk factors generated by the in-house machine learning team. The increasing penetration of the IoT technology among various end users is helping to increase the adoption of machine learning as a service platforms to identify new trends and potential threats or challenges in its implementation.
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The ever-changing retail sector demands agility as well as better relationships with clients. Therefore, retailers are using machine learning services to create remarkable shopping experiences for customers. Deep-pocketed retailers are hiring analytic consulting firms to gain insights that are useful for marketing purposes.
Smaller retailers are also joining the bandwagon to leverage the power of data to understand customers through cost-effective cloud-based machine learning services. Such findings are translating into incremental opportunities for participants in the machine learning as a service market across the world. With the help of machine learning, the systems or software automatically learn from each promotion sent to a customer, again using that learning or past data in the next iteration.
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