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.

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.

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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)

Machine Learning and BlockChain

If there’s a topic which never moved from its “trending” status, that would definitely be related to cryptocurrencies and blockchain. In fact, there has been a significant fluctuation within major players (Facebook, in particular) for what concerns applying their algorithmic technology to the well-renowned blockchain. This is incredibly important to properly understand, as it has been stated that the next couple of years will be pivotal for what concerns the matter, telling us if the blockchain will prevail and develop a universally shared processing method.

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Python Applied to Other Coding Languages

As told above, Python-based tools have started to contaminate the web development world, leading us to the natural conclusion that such programming language can be combined with other front-end related ones (HTML, CSS and Javascript). In the future, this will definitely be applied to more and more plugins, tools and overall architectural software, almost leading Python to the top as the most required programming language within the IoT. It’s important to state the fact that Python is extremely complex and does require major processing power in order to properly work, which will most likely slow down your site’s speed or your software loading time.

The Market Value

With every trend comes, of course, a market evaluation. Machine Learning as a whole has currently peaked $2 billion in terms of industry value, leading us to the conclusion which is univocally telling us that there will be a substantial growth within this segment in the next couple of years. In order to better understand this is important to:

-Take environmental variables into consideration, given the fact that automation is something which is very appealing for many companies ranging from the fintech world to a far more technological-niche related one.

-Understand how the development market moves, since the fact that machine learning is a “trendy” topic and, therefore, it’s actively looked after by many companies (as said above, Apple and Amazon have invested a lot of money in the matter during the past year)

-Process the requirements for such professionals, as the training for such figures, may be hardly expensive due to the nature of such knowledge.


Coming back to the market value mentioned above, in order to proper picture the industry we want to analyse, a big step would be to analyse their stocks value, in order to properly asset if their technological development had an impact on their fundings and overall market value. Stocks could be also calculated within a blockchain environment, as they’re becoming more and more popular (and also because they’re incredibly related to the topic in question).

As pointed out by many industry analysts, in fact, the stock value of a machine learning startup in 2020 could grow by 700%, given the mole of investments which are currently being carried out by the companies mentioned above.

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The Aerospace Industry

NASA’s architectural developers and planners have stated multiple times how the use of machine learning algorithms have helped in both processing and planning the data which was gathered by rovers, satellites and telescopes all around the world and the universe.

The aerospace industry has always been the pinnacle of technological research and applications under every single possible aspect and, of course, machine learning isn’t an exception. Processing such amount of data (imagine that a couple of high-resolution pictures could take around a terabyte of memory) within the shortest timeframe is definitely something which can be done by an autonomously learning tool and, therefore, it’s the reason why this technology is so looked after within this field.

Not Just The Automotive Industry

As listed above, the overall “driverless experience” has been incredibly looked after in the last couple of years, given the fact that all the major players (TESLA and Volvo as said above) have invested a major amount of money in it. This form of “driverless” technology is also applied to other transportation segments, such as planes, which have seen a net development in autonomous vehicles in the last couple of years (drones, especially). Of course, given the delicacy of this segment, it will take a couple of years before this driverless experience is implemented in 737 flights and “normal” routes, given the fact that the human requirement, in case of any failure, is still a must.

To Conclude

Although these are simple examples, they are definitely the major ones within the segment. 2019 is definitely set to be a pivotal year for what concerns technological development and machine learning will definitely be the main character.

Many are the companies which are acquiring startups with avant-garde products and tools, and this is definitely also exciting for the savvy Python developer, given the fact that these years will set the foundation for what concerns the development of such technology as a whole. With all these pieces of information in mind, machine learning is definitely something less obscure for the one who has no idea what the technology is about and, hopefully, it will clear all the doubts related to the matter.


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