The technological sector has drastically changed in the last couple of years: with the rise of many different programming languages and with the implementation of more and more mobile-friendly strategies when building a particular piece of software/when planning hardware updates, understanding such pivotal industry changes is vital at this stage. If there’s one aspect which has impacted the technological field as a whole, ranging from startups to big enterprises, that would definitely be machine learning. Let’s analyse the matter in more detail.
Where Is Machine Learning Applied?
Machine learning has peaked to its maximum last year when Python developers have been featured in many industry reports as one of the main professional figures within the technological sphere. To better understand this statement, it’s important to list how machine learning is being used, currently. Software development currently occupies around 65% of the entire work focus from Python developers and the reason is related to the fact that, in many cases, many pieces of software are implementing (or, at least, trying to) automated features which require a deep understanding of Python as a whole. Of course, there is still a net 35% left, which divides into experimental fields, such as front-end development, warehousing management and more.
Why Is ML So Important?
Big data is a term which is currently used a lot in today’s technological field, especially after those scandals which involved Facebook last year. The way machine learning flows with big data is related, mainly, to the way such data is gathered: by exploiting the python architecture of such data flow is possible, in fact, to store personal information onto servers and, once this is done, updating content, catalogues or retargeting algorithms becomes pretty easy, from a webmaster point of view. ML is still at an almost embryonic status, but the matter is definitely set to change in the next couple of years, with the implementation of many more Python-related features within different pieces of software.
The Mobile Field
As said above, the implementation of desktop-related technologies within the mobile field is vital, as many app developers have stated. In fact, there are several signals of automatic features, mainly Python coded, being applied to iOS and Android builds, leading the entire market to a basic (but not really) adaptation on the matter. In the future, we will definitely see more and more updates on what concerns the development of automatic features within this field.