What is machine learning?

Machine learning is a subfield of computing and a branch of artificial intelligence that develops techniques to promote learning by smart agents.

What does a machine learning engineer do?  

A machine learning engineer (ML engineer) is a person responsible for developing, applying and supervising data management and machine learning models within a company or organization.

The specifics and scope of their work depend on the organization and its objectives. Their functions may include the following:

• Researching, adapting and designing machine learning schemes.
• Implementing appropriate algorithms for each case.
• Selecting and compiling data sets.
• Constantly training and retraining the machine learning systems and models.  
• Observing the behavior of the data to increase its efficiency by refining it, improving its organization, etc.
• Statistically analyzing data and results to optimize models.
• Drawing conclusions applicable to other areas of the company or organization.
• Developing applications.

What training does a machine learning engineer have? 

Currently there is no specific training for machine learning engineers, although there are complementary training courses that can be accessed by certain professionals who wish to specialize in machine learning. Usually, machine learning engineers have trained as IT specialists, programmers, computer engineers, statisticians or data analysts.

What skills do you need if you want to be a machine learning engineer?  

The main technical competences that a machine learning engineer must have are:  

• Programming languages.
• Statistics and descriptive statistics.
• Probability theory.
• Mathematical calculation.
• Matrix algebra.
• Big data.

The profile of a machine learning engineer combines technical skills with human and analytical skills. Some of the most important soft skills for machine learning engineers are:

• A great capacity for observation and analysis.
• Willingness to continue learning.
• Critical thinking.
• A capacity for communication and coordination with other departments.

The development of machine learning is also leading to a need for specialists. The knowledge that machine learning engineers have in other areas, such as linguistics, social sciences or medicine, and the way in which specialists in such areas can be incorporated in work teams is thus becoming increasingly important.