Machine learning, one of the spearheads of artificial intelligence, opens unimaginable perspectives in the current digital era. Within the context of the great data, it is bringing great advances in the most different areas, in a sum and continues what does not seem to have an end.
All this thanks to having received a great impulse as a result of the advances in different fields and disciplines, such as mathematics, computing power, Internet of things sensors and cloud computing. A scenario that, on the other hand, allows its use as a service.
Their applications are innumerable, surprising and, in many cases, exciting. Indeed, the fields of application are enormous, sometimes unpredictable. Sectors such as e-commerce and marketing are just a small sample of the possibilities of the use provided by a machine learning project.
Machine learning: endless applications
The list of possible applications is almost unlimited but, above all, it is an open list that, surely, will have many surprises. Among other uses, automatic learning algorithms make possible the systems of recommendations in social networks or searchers, as well as in the platforms of companies like eBay or Amazon.
Likewise, automatic learning helps in online security, facilitates the detection of faults, consumer trends or potential customers, in addition to being able to predict urban traffic, diseases, perform better diagnostics, voice recognition or, for example, open the Ddoor to communication with machines.
According to Satya Nadella, CEO of Microsoft, it also helps improve productivity and efficiency of workers and companies in general, as well as improving business figures thanks to its key role in identifying behavioral patterns.
It reveals itself as a great ally, according to Nadella:
But for this, regardless the orientation that implies each project, it is important to convert the data that are generated to the companies in valuable information.
Machine Learning as a Service Option
That is when we go from the world of the theoretical, with incredible possibilities to carry out projects, to a tangible reality: ours. And it is also then when we must consider that both making the mistake with utopian goals as well as abstaining only for fear of failure.
Without recognizing that the application of automatic learning systems is complex, it is also true that the application of machine learning as a service can make things much easier.
Given that the technology and the data are there, waiting for us to take advantage of them, betting on their use is a capital decision, since it means to take an advantage of a golden opportunity in order to make better decisions.
Therefore, launching it in a prudent and studied way, whenever benefits are foreseen, is the first step to add points to the much-desired competitive advantage. A decision that, for many companies, means using machine learning as a service, in the cloud and without too many complications.
What are its advantages?
Although with some limitations, today’s machine learning is accessible to those who wants to take advantage of its possibilities through the formula of machine learning as a service, a series of services in the cloud that include automatic learning tools.
In the market we will find different suppliers of cloud computing MLaaS that, on the one hand, carry out the calculation in their respective data center from the use made with tools of very different type. Among them, predictive analytic tools, data modeling APIs, automatic learning algorithms, data transformations, deep learning, facial recognition, data visualization or, for example, natural language processing.
The strength of these services lies precisely in this easiness of use, since customers can start applying it in the cloud, without having to invest in the installation of software on premise. Like any other cloud service, it can be used either in the cloud or in a hybrid way, in which case machine learning must be integrated with the local IT infrastructure.
Great potential for business
In terms of increasing sales and improving business decision making, the fields of application of machine learning cover very different areas and can be translated into projects of the most varied and ambitious.
In practice, however, projects need cultural support at an organizational level that is not always easy to achieve. Also, their accuracy depends on a combination of factors, like needs to be taken, creativity or, for example, available technological and human resources.
Providing value and competitive advantage, in fact, requires skilled workers, experts in converting data into valuable information through predictions obtained with the help of automatic learning systems.
While machine learning as a service provides us with automated, scalable systems capable of evaluating and improving analytical processes with little human intervention, we will only succeed with the skills of the experts. Fortunately, you can access these resources without having to invest in complex infrastructures.
By using machine learning services in the cloud and a small team we can start: design a first strategy and build our first working models for the realization of predictions that start to give results.
The goal of a machine learning as a service project is to understand our own data, identify patterns and obtain valuable information through automated processes, thanks to the platform’s computing capabilities. All this, obviously, in order to grow the business.
With these minimum resources (these will vary according to the solutions offered by each provider), the use of machine learning as a service has great potential for companies, since the models update themselves continuously, gaining time and Reducing costs.
The challenge, even with the help of machine learning as a service, requires dedication and countless efforts that are truly worthwhile if we consider the advantages it can give. Basically, it is about creating predictive models that provide added value to the organization through achievements related to the optimization of business processes.
Thanks to a better knowledge of the clients, it will be easier to be loyal and attract potential buyers or users, as well as to find new revenue channels, providing cost savings, as well as identifying potential threats, reducing risks and vulnerabilities. In fact, automated learning can improve the security of critical data in an organization.
Great prospects, but a lot to go
Although the success cases are numerous in the use of machine learning, it is also true that there is still a long way to go. In this sense, a recent report by The Drum explores the application of machine learning in the resolution of commercial challenges to conclude that its importance is increasing in an important way, being one of the priorities of the agenda of the sector.
Meanwhile a survey by Wakefield Research and Demand Base revealed that 80 percent of marketing executives attribute artificial intelligence to a revolutionary role in this area within five years.
However, almost a third of these professionals declared they did not know how to take advantage of it, in the same line as a Forrester Consulting study, which detected this same lack of knowledge. A lack that, probably, the formula of the machine learning as a service is called to supply.