In-depth analysis of Machine Learning as a Service
Programming suites and some AI applications in the sector proved that utilizing machine learning gives you the higher ground. However, neither applying nor conveying it is not easy due to a couple of technical factors. Thus, a new segment – new paradigm of machine learning arose from the need of the market: Machine learning as a Service, MLaaS.
MLaaS concept is born from the need of easing up the hard process of applying machine learning to any system. Although almost everyone in the sector would advice you to utilize machine learning to “leap toward” or to “transform”, no one tell how to do that! Machine learning is not a soft process that would be finished in the blink of an eye. Nonetheless, latest applications proved that one does not (almost) need an expert on building a machine learning algorithm as most companies don’t need to construct it; but, need to utilize it. Acceleration of the machine learning discourse shows improvements regarding companies from every scale.
Where is Machine Learning in 2019?
A couple of days ago, I read an old Times article (1953) that giving the news about a learning vending machine which learns from the patterns of customers and the time of the day to give proper advice. Well of course this is not even near where we are right now. However, the concept is, as can be put it that way, a very old one. Initial seeds of machine learning are as old as the AI itself (Alan Turing’s AI). Thus, from the 1940's onwards up until the first commercial use (Deep Blue), machine learning is constantly changing and improving. Alas, as this is a continuous process in the discourse of multiple doctrines, we should not choose a single point in history (say 2019) to observe the progress of ML. Nevertheless, multiple doctrines notion gives a hint here: in 2010s we had learned that we must work in collaboration with other areas to seek an advancement in any sector, specially in ML.
Where it is Heading Towards?
Heading of the Machine Learning is almost unclear but never timid. That is, it goes from everywhere to anywhere as sectors once called irrelevant to ML build up around it. More than thousands of leads are generated through ML as its application area is enlarged and challenges of application are one-by-one tackled. One of the best descendants of the ML achieving those is Machine Learning as a Service. We are sure that ML is exceptionally heading towards that way.
Differences and Benefits of MLaaS
Main difference of the MLaaS is, of course, its application method. The former method of utilizing machine learning needs a pack of ML expert programmers, a data scientist, and other IT appliers to work. Not only did they costly to have but also, they were hard to find. Therefore, almost every dream of applying machine learning was drowned in the cost and HR gaps. However, the biggest difference and the benefit of MLaaS over the ML is that it has no need of hiring experts to build it. Once it has been built by a MLaaS company, it can be used effortlessly by every company from every scale. Cloud computing (cloud management in this example) gives the best out of ML in terms of costs, easiness to apply, and sleekness to use.
Enhencer as a MLaaS solution, gives the best alternative of the machine learning as a service through automated machine learning. Only having a data set, an overseer to results, and an accountant is more than enough to utilize machine learning in any sector, all thanks to Enhencer. Machine learning is always thought as dead-end for advancement. However, it is just a beginning of an initial process. That is, if you happened to construct an ML model, you are not done. There must be model selections, significance tests, and re-constructing according to needs. Thus, heading into that road is totally harder than applying an already constructed and tested machine learning solution – Enhencer MLaaS.