When it comes to Machine Learning, it’simportant to understand the story which stands behind such a piece oftechnology. In fact, ML developed properly since 2015, when Python developersfound their way in a market which was (at that time) colonized and saturated byfront-end (Javascript in particular) professionals. With the current mobiledomination, let’s analyse how impactful ML and Deep Learning are in such field.
HowDoes Machine Learning Apply To Mobile?
Machine Learning as a whole doesn’t simply apply to automate processes and robotics, as many think. In fact, there are a lot of tools which are focusing on building data and automatically store it within particular positions. Data science is, nowadays, a very important business field and its mobile application is, therefore, incredibly relevant. In order to state how impactful ML is, currently, within the mobile field, we should look at how the employment world is approaching the matter: in 2017, the requirements for Python developers grew for a net 23% within triple-A companies like Apple, Google and Amazon, clearly stating the fact that such technology is actively looked after by the top players.
AnArchitectural Matter
Autonomous learning-related technologiesdefinitely sound like something which is a bit obscure for someone who doesn’tknow how they actually work. Currently, the majority of these ML-basedapplications are simply fixing the manual processing related to data, rangingfrom cookies to email surveys. In fact, a major field which has been impactedby ML would definitely be the eCommerce world and, in general, those companieswhich are heavily relying on website-based enquiries. Web personalization, which is a process thatautomatically tailors catalogues and pages onto users’ preferences, has seen anet growth in the last couple of years, either in a startup-related dimensionand in a far bigger one, like Apple’s.
TheGoogle Factor
In a recent interview, Google’s webmastertrends JohnMueller stated how important is automation research for the company,stating that “there will be a massive machine learning growth within ourflagship apps like Maps”. This, combined with the fact that the companyhas recently admitted how they will prioritize the mobile field (especiallygiven the fact that they recently launched a complete mobile-based index) is aclear statement of the direction Google wants to take. Analysing what thebiggest web-related company does is vital in order to understand how the futurewill be (or most likely will).
ToConclude
When it comes to Machine and Deep Learning,there are significant movements for what concerns their mobile applications.It’s important to monitor what the market’s trends are in order to betterunderstand the progressions of such technology and, given the current state ofit, we can safely say we will hear more and more development news on the matterin this 2019, marked as the pivotal year for ML by many.
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