The Evolution Of Machine Learning In 2019

As we all know, Machine Learning has become a trending topic within both the tech field and, also, from a business point of view. Many are the companies who are actively looking into automating their warehouses, their tools and also (surprisingly) their management.

To break down machine learning’s trends isn’t the easiest task, but let’s see what the major trends will be throughout this whole year.

Automotive ML

Companies like TESLA and Volvo are definitely leading the market when it comes to automotive innovation: there have been, in fact, significant investments within the ML (machine learning) topic, especially for what concerns its application in the trending topic that is known as “the driverless experience”.

TESLA’s autopilot, in fact, is using a pretty advanced form of SLAM (Simultaneous Localization And Mapping) algorithm which basically runs projections in real time based on what the installed sensors (currently version 3) are gathering from the surroundings. ML is definitely a major topic and, since the driverless experience is set to be an industry standard before 2030, it will definitely be considered from a business point of view throughout this year.

Website Personalization

Another major topic that has been highly impacted by Machine Learning as a whole would definitely be related to big data and, consequently, data science. Web personalization is that branch of web development which focuses on creating tools which are able to optimise listings, catalogues and websites in general onto such data gathered by cookies, emails and surveys.

This is extremely important since every major player within the digital field is trying to bypass SEO and PPC-related techniques in order to boost their UX and, most importantly, their Conversion Rate. Many top players within the fast fashion industry are utilising web personalization tools, Zara in particular. This is extremely important from an industry insight point of view, as it basically states the fact that Zara is the most technologically advanced company within the field.

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The Mobile Field

As known by many, currently almost 60% of the entire IoT (Internet of Things) is related to mobile traffic. This doesn’t just focus on people browsing the internet from their mobile devices but also covers apps which are internet based (social networks, for example) and everything in between.

Machine learning is definitely moving towards the mobile realm, as pointed out by many mobile app development companies which are actively looking into applying Python-based strategies in their software architecture. This is definitely something which will be hardly explored in 2019, especially within the day-to-day usability field for what concerns mobile apps: Google has, in fact, point out how they are implementing SLAM (mentioned above) strategies within Maps, in order to give better projections of the surroundings.

The Data Processing Workforce

Machine learning could be an obscure matter for the one who doesn’t know that such technology applies to more fields than just robotics and automation. Nowadays, in fact, Machine learning has reached the data management segment by optimising the storage of important pieces of data within websites and companies in general.

The figure of the data scientist has, in fact, reached its highest level in 2018, being the most looked after position in companies like Apple (28% of their job offers were related to data science and machine learning) and Amazon (given the fact that AWS has increased its workforce investing in European countries like Italy and Germany, for example).


As said above, robotics is a segment which has seen substantial development in both technology and finance in 2018. What once was seen as a merely futuristic matter is nowadays impacting many different industries, especially the ones with a heavy warehousing workforce. Let’s take the Chinese titan Alibaba as an example: in 2018, the company was able to boost their productivity by a net 70% by simply installing 40 robots in their main warehouse. This, of course, moved the market from both a technological point of view but also from a business one. (as, in fact, it actually moved the robotics matter to a more “mainstream” market)

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