Educating the machines: this is what a machine learning specialist does
Programmers, developers, data analysts, big data architects: so many new positions make up the world of new digital professions. We have been saying it for some time: these are the jobs of the future. But there is one role in particular that is advancing up paths and tracks different to those we have seen thus far. The machine learning specialist is close to the data science sector and one of today’s most sought (and highest paid) professionals. To the extent that the website Stack Overflow labels it the fourth highest paid position in the software industry, lining up alongside data scientists and developers with backgrounds in statistics and maths.
The role of machine learning specialist requires a combination of statistics and IT, to develop learning algorithms that improve continuously.
But who are these machine learning specialists and what exactly do they do? This professional specialises in machine learning development, a branch of artificial intelligence that focuses on creating algorithms that “learn” by taking data they have been fed and adapting it to make predictions. Here are some practical examples. Self-driving cars perhaps represent the very essence of machine learning, but suggestions for online offers (from, say, Amazon or Netflix) and the use of social networks to measure a company’s reputation (which combines machine learning with the creation of linguistic rules) are also some of self-learning’s many applications.
The role of machine learning specialist requires a combination of statistics and IT, to develop learning algorithms that improve continuously. Software recruitment service Talent Lyft specifies that machine learning experts are expected to design and develop algorithms, carry out explanatory data analyses, discover, design and develop analytical methods to facilitate new forms of data and information processing, generate and test working hypotheses, provide technical support for the activities involved in program management and business development, and manage knowledge so that it can be shared with the relevant community.
Why is the machine learning specialist role so vital? The companies that have a grasp on the importance and power of Big Data have equipped themselves to be able to centralise the huge amount of data they previously stored in archives in larger databases. Now, though, they need specialists who can manage that data and IT technicians are not sufficiently equipped for the task. The interesting thing is that, when it comes to other professions, machine learning has not attracted only IT experts: it is potentially also a field that requires mathematicians, statisticians and programmers.
Today, just 30% of those undertaking academic studies attend courses in machine learning or data science.
What is the path to becoming a machine learning specialist? As has (unfortunately) been noted, the rate at which the content and structure of university courses can be updated holds them back: they cannot keep up with the evolution of technology and AI. In a survey carried out by Kaggle – a community of data scientists and machine learners – Today, just 30% of those undertaking academic studies attend courses in machine learning or data science. The fact is that even a university course specific to this topic could never be complete. The content develops so rapidly that machine learning specialists are the quintessential example of why “lifelong learning” and “continuous professional development” are so important. Being resourceful and being able to teach oneself thus become vital skills: in fact, 66% of those that Kaggle surveyed said they had learned the essentials themselves, while little over half took online courses.
There are many such courses available at the large universities, such as Stanford, as well as on platforms like Coursera, Udemy and Springboard, a search engine for online courses that also provides a list of universities and companies offering training in this field. The video classes from this MIT summer camp give an interesting (and free) overview of the matter, exploring how the human brain works and how that can be reproduced in machines.
Machine learning specialists are the quintessential example of why “lifelong learning” and “continuous professional development” are so important.
In any case – and fortunately – machine learning is an area very much open to people with competencies in all sorts of sectors. Getting a foot in the door to the AI sector is easier than it might seem – at least, that is what Andrew Ng tells us. He is a pioneer of deep learning, a search method based on the hierarchical classification of factors and concepts and on data representation learning. Why? Simply put, by increasing the amount of data available, the algorithms analysing it become increasingly intelligent and intuitive.
The most important factor in machine learning is in fact repetition, because the more the machines are exposed to the data, the better their ability to adapt themselves, learning from previous instances of processing to produce results and making reliable and reproducible decisions.
What is this profession’s weak point, then? Due to its being such a complex sector, one that is yet to have been integrated into companies’ daily operations, and the fact that managers and employers are still not sure what it requires and how it can be applied, some companies may not be able to provide sufficient valid data to obtain good results. Their demands may be unspecific or unrealistic. The natural and also ideal route for machine learning specialists is therefore within companies that grew up online and are used to collecting huge quantities of data on their users’ behaviour. Those companies use tests such as the A/B test (hypothesis testing commonly used for UX design) to improve their services. Companies that want to be competitive and snatch up the best future talents, should take inspiration from this example, implementing any associated processes accordingly. But they should remember that this is just one of many examples they should follow.
Which countries are best for finding work in this field? The US, of course, but also countries like India and China. Those looking to work in this field should seek out tech and internet companies, but also be on the look out for roles in finance and insurance firms and, finally, in the world of academia. Acquiring the necessary skills is hardly child’s play, but doing so opens up infinite opportunities, so it is worth taking up the interest sooner rather than later.