Machine learning is a computing technology that provides computers the ability to learn and modify their analytical functionalities when exposed to new data sets, without being explicitly programmed. It is used to capture data and consequently run discrete modelers to create patterns for subsequent processing, analysis, and interpretations required in real-time decision making. MLaaS provides a subscription-based model or an open-source platform for this technology, thus opening up the domain of machine learning and predictive analytics to developers and potential end users from all sectors.

Vendors who fall into this category receive high scores for most of the evaluation criteria. They have strong and established product portfolios and a very strong market presence. They provide mature and reputable data integration tools and have strong business strategies.

They are established vendors with very strong business strategies. However, they offer less products in the market. They focus on a specific type of technology related to the product.

Innovators are the vendors who have demonstrated substantial product innovations as compared to their competitors. They have very focused product portfolios. However, they do not have very strong growth strategies for their overall businesses.

The emerging players specialize in offering highly niche solutions and services. They do not have strong business strategies as compared to the established vendors. MACHINE LEARNING AS A SERVICE MARKET ANALYSIS, BY COMPONENT DATA STORAGE AND ARCHIVING The mechanism of machine learning algorithm can be described by using three learning principles: supervised learning, unsupervised learning, and reinforcement learning. These learning principles need data as an input parameter to predict the solutions of various problems. The machine learning algorithm of these learning principles gather data from data storage and archiving software tools and create a mapping functions on data to transform input to output. In addition, they discover hidden patterns in data and data sets to predict the final outcome of various problems. MODELER AND PROCESSING Over the last decade, machine learning technologies have evolved into “algorithms” formulated from diverse fields including statistics, mathematics, neuroscience, and computer science, making them commercially viable and computationally robust. CLOUD AND WEB-BASED APPLICATION PROGRAMMING INTERFACE In machine learning algorithm, requirement of data is an essential input parameter. Some of the business verticals such as banking and financial services need a large amount of data instantly to predict the market behavior. Machine learning algorithms get very less time to predict solutions after gathering data from data storage and archiving software. To overcome this complexity, machine learning algorithms create an interface between cloud and the application platform. With the help of this interface platform, machine learning algorithms can access a large amount of data or data sets via cloud.