Data is without a doubt the driving force behind the fourth industrial revolution. Machine Learning and Artificial Intelligence are receiving much more attention from all stakeholders as most of the sectors of the economy are becoming increasingly data-driven. Likewise, enterprises employ data science practices in order to convert raw data to knowledge by means of machine learning and data mining techniques. Nevertheless, developing a proper data science infrastructure and building a competent data science team is still a very challenging task, as there is a proclaimed talent gap in Big Data and machine learning, and also there is a dearth in domain experts who are capable of working with large volumes of data. This has recently given rise to the emergence of cloud-based infrastructures for machine learning and data analytics, which enable companies to access machine learning resources and toolkits over a provider’s infrastructure, rather than having to deploy such resources in-house. In practice, such cloud-based infrastructures provide the means for outsourcing part of a data analytics task to a third-party, much in the same way enterprises can nowadays access a wide range of computing and storage resources through a cloud provider (e.g., based on the Infrastructure-as-a-Service (IaaS) model). In the case of machine learning and data analytics resources, the respective cloud-based platforms are conveniently called Machine Learning as a Service (MLaaS).
MLaaS is a general term, which refers to offering machine learning and data analytics tasks over a cloud platform. Typical examples of such MLaaS tasks are those foreseen in popular data mining and machine learning methodologies such as the Cross-Industry Standard Process for Data Mining (CRISP-DM). They include data pre-processing, model training, model evaluation, as well as the visualization of the results. In the scope of an MLaaS platform, these tasks are executed from remote in an automated or semi-automated fashion. Moreover, MLaaS platforms enable access to the results of these tasks through REST APIs, which facilitate the integration of MLaaS functions with other (in-house or cloud-based) elements of an organization’s IT infrastructure.
Based on the execution of the above-listed machine learning functions over a provider’s cloud platform, enterprises can accelerate their data science deployments, as they are able to deploy models with limited data science expertise. Still, enterprises will have to employ business analysts and data mining experts, but in lesser numbers, as the required IT infrastructure and tools are available and accessible as MLaaS.
The rising popularity of the MLaaS paradigm is reflected by the fact that all major cloud vendors (e.g., Amazon, Microsoft, Google) are offering machine learning services to their customers. The development and evolution of their MLaaS platforms are driven by the following principles:
Overall, MLaaS platforms provide versatility, scalability and deployment ease to developers and deployers of non-trivial machine learning systems. This is the reason why they are gaining popularity and traction in the machine learning ecosystem.
Apart from MLaaS platforms, we are also witnessing the emergence of cloud-based machine learning applications i.e. cloud applications that apply machine learning models in order to produce their results. Some of the most prominent examples of such applications follow:
The above listed applications are only the tip of the iceberg and more sophisticated applications that mix sensor data, social media data, batch transactional data and more, are possible with MLaas. In the near future, we will see a further proliferation of machine learning applications, which will be delivered as a service. Likewise, we will witness an advancement in the functionality, reliability, and ease of use of MLaaS functionalities and tools. It’s therefore vital that the data scientists and data-driven enterprises should consider the merits of MLaaS platforms, along with the best ways to take advantage of them.
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