Still unknown, the job of Machine Learning Engineer is more and more sought after in companies. Find out everything you need to know about this profession, halfway between the Data Scientist and the Software Engineer...
According to the “Jobs and AI Anxiety” report, 30% of CEO are already using artificial intelligence and Machine Learning. Moreover, 53% of them plan to adopt these tools in the next three to five years.
AI is permeating all sectors and is one of the must-have technologies of this century. However, to exploit these revolutionary tools, it is necessary to call upon highly qualified experts. The Machine Learning Engineer, or ML Engineer, is one of these specialists.
What is a Machine Learning Engineer?
The job of a Machine Learning Engineer is similar to that of a Data Scientist. Both work with large volumes of data, have strong data management skills, and can perform complex modeling on dynamic data sets.
However, the similarities end there. While Data Scientists produce reports and graphs to present the results of their data analysis, Machine Learning Engineers design “self-running” software to automate predictive models.
Each time the software operates, it uses the results to perform future operations with ever-increasing accuracy. The machine “learns”, which is why it is called Machine Learning.
Among the best-known examples are the recommendation algorithms of Netflix, Amazon, and Spotify. Each time a user watches a video, searches for a product or listens to music, the algorithm feeds on the data. Its recommendations become more and more precise, without the need for human intervention. The role of the Machine Learning Engineer is to develop such algorithms.
To put it simply, the job of a Machine Learning Engineer is a mix between a Data Scientist and a Software Engineer. In large companies, the ML Engineer frees the Data Scientists from engineering tasks so that they can focus on mathematical modeling and tasks requiring real scientific expertise.
What is Machine Learning Engineering?
To fully understand the role of the Machine Learning Engineer, it is important to understand what Machine Learning Engineering is. It is the use of principles from software engineering in combination with data analysis and data science methods to create Machine Learning models.
For example, an ML engineer working for YouTube is responsible for developing the platform’s recommendation algorithm. He will then have to develop a pipeline and integrate it into YouTube so that the end user can benefit from relevant recommendations.
Why are Machine Learning Engineers so sought after?
With the rise of technologies such as the Internet of Things, social networks, and the mobile internet, companies in all sectors are now flooded with data. This information is a valuable resource, as its analysis allows us to make better decisions or to better understand the consumer.
However, to analyze such large volumes of data, often unstructured, it is necessary to use Machine Learning. Depending on the nature of the data, ML can be applied in different ways.
The algorithms allow for example the recognition of images and speech. This allows the content of images to be analyzed automatically and labels to be assigned. Similarly, audio speech can be converted into text using speech-to-text. Unstructured data can thus be transformed into usable information.
Machine Learning also allows connections and associations to be made between data. This can be very useful to decipher consumer expectations. For example, a ML model can predict that a customer who bought product A will like product X. This makes it possible for e-commerce companies to recommend products to their customers based on their previous purchases.
In addition, Machine Learning is being used in finance to predict risk and prevent fraud by detecting it in real-time. Vast volumes of historical data can be analyzed to make predictions, determine the potential of an investment or anticipate a default.
As you can see, Machine Learning is very useful in a wide variety of fields. That’s why ML Engineers are highly sought after in all industries…
What are the expected skills for such a position?
The job of Machine Learning Engineer appeared very recently. That is why there is no clear and precise definition of this profession yet.
From one job offer to another, the expected skills can vary greatly. Generally speaking, companies are looking for a profile that combines the skills of a data scientist and those of a programmer.
Conversely, programmers with strong Data Science skills can also become Machine Learning engineers. However, it will be necessary to work in the data domain beforehand.
Most of the time, an ML Engineer needs to have an advanced degree in computer science, mathematics, statistics, or any other related discipline. Mathematical concepts such as linear algebra, Bayesian statistics, or group theory must be mastered.
This expert must also have skills in data modeling and data architecture. He or she must also master the programming languages Python, R, and Java, and handle the most popular Machine Learning frameworks such as TensorFlow or Keras. Big Data platforms, such as Hadoop, hold no secrets for him.
The Machine Learning engineer also has experience in an Agile environment. Finally, he or she has a talent for written and verbal communication, to be able to collaborate with other employees from different departments of the company.
What is the salary of a Machine Learning Engineer?
Again, it is difficult to determine the average salary of a Machine Learning Engineer due to the recent nature of this profession. According to Glassdoor, the average annual salary is $114,000 in the United States.
This job is at the crossroads between the roles of Data Scientist and Software Engineer. By way of comparison, according to Glassdoor, a Data Scientist in France earns an average of $49,650 per year. A software engineer, on the other hand, can expect to earn $42,865 per year.
How do I get to be a Machine Learning Engineer?
To become a Machine Learning Engineer, it is recommended to start a real project to gain experience. For example, it is possible to retrieve a model from a platform like Kaggle and turn it into a full application.
In the process, you will be asked to perform most of the tasks that make up the daily life of a professional. A project like this will greatly enhance your resume and will make it easy for you to find work by demonstrating that you have the skills required for the job.