When it comes to Machine Learning, it’s
important to understand the story which stands behind such a piece of
technology. In fact, ML developed properly since 2015, when Python developers
found their way in a market which was (at that time) colonized and saturated by
domination, let’s analyse how impactful ML and Deep Learning are in such field.
Does 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.
Autonomous learning-related technologies
definitely sound like something which is a bit obscure for someone who doesn’t
know how they actually work. Currently, the majority of these ML-based
applications are simply fixing the manual processing related to data, ranging
from cookies to email surveys. In fact, a major field which has been impacted
by ML would definitely be the eCommerce world and, in general, those companies
which are heavily relying on website-based enquiries. Web personalization, which is a process that
automatically tailors catalogues and pages onto users’ preferences, has seen a
net growth in the last couple of years, either in a startup-related dimension
and in a far bigger one, like Apple’s.
In a recent interview, Google’s webmaster
Mueller stated how important is automation research for the company,
stating that “there will be a massive machine learning growth within our
flagship apps like Maps”. This, combined with the fact that the company
has recently admitted how they will prioritize the mobile field (especially
given the fact that they recently launched a complete mobile-based index) is a
clear statement of the direction Google wants to take. Analysing what the
biggest web-related company does is vital in order to understand how the future
will be (or most likely will).
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 better
understand the progressions of such technology and, given the current state of
it, we can safely say we will hear more and more development news on the matter
in this 2019, marked as the pivotal year for ML by many.
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