Personalized advertisements, language assistants, streaming or even CT scans for early medical diagnosis: Machine learning (ML) has become an integral part of our everyday lives. And that is just the beginning. ML is one of the most important trends in digitization and a driver of future growth. But what exactly is ML and how does the job description look like?


Machine Learning explained quickly

💥​​Machine Learning is a subfield of artificial intelligence (AI) that is playing an increasingly important role. In machine learning, algorithms are trained to automatically recognize patterns and connections from existing data volumes. They improve continuously and are also able to independently solve problems from the patterns.
Smooth cooperation between the ML Engineering and Data Science departments is important to ensure that ML systems function correctly and become more and more efficient. Ideally, both areas flow into each other if both work hand in hand in order to optimally combine their knowledge from both areas. They bundle their knowledge and thus become more efficient. This does not make it easy to define the disciplines of the job description. Especially since the areas of application can vary in practice. Important, however: As an ML engineer, you should be interested in teamwork and in constant communicative exchange.



What are the tasks of ML Engineers?

The range of tasks includes topics from the two related professional fields: data science and software engineering. It is primarily the task of machine learning engineers to bring the prototypes developed by the data scientists to market maturity, i.e. to bring them into production. The experts when it comes to AI algorithms are therefore the data science team, while ML engineers are the professionals with the software tools. You master these tools in your sleep to make the ML models usable. Important at this point: Machine learning engineers write software. These models or algorithms are constantly updated and optimized with the help of the data and continuously tested.


ML engineers deal with the following questions:

• Which deployment choice is best for the problem to be solved?

• How do I ensure that my model is always reachable and always predicts correctly?

• How do I optimize the speed of these processes?

The range of tasks is in continuous development, as this is an absolute newcomer job.


The job description and tasks at a glance:

• Bring machine learning models into production

• Improve or maintain existing machine learning models

• Co-responsibility in the data team for the entire life cycle (development, deployment, monitoring and maintenance)

• Scaling theoretical data science models into production-ready models

• Checking the data quality

• Conducting machine learning tests

• Monitoring of the current status and alerting if something goes wrong.

• Close cooperation with data science, software engineering and data engineering
  
  

What skills are required?

Logically, the basis of everything is a passion for coding - ideally also a penchant for solving problems in creative ways, trying out new ways and not being afraid of failure. Computer and information technology knowledge is elementary, knowledge of mathematics and stochastics is also ideal. A lot of other skills are also important for this.

You can find a small list below:

• Good understanding of mathematics and statistics

• Experience with common methods of machine learning, and their evaluation metrics and best practices

• Software Engineering & Data Engineering

• Model Deployment

• Monitoring und Alerting

• ML-Testing

• Working with Structured Query Language (SQL)

• Project management and agile methods

• Experienced handling of programming and command of common programming languages such as Python or Java

• Proactive problem solving and understanding of cloud services

How much does a ML Engineer earn?

This job is the future. With increasing technological progress and constantly growing complexities, the demand for ML engineers on the job market is also increasing. So the hype is yet to come. The salaries are already impressive, because even juniors start at around €50,000/year, depending on the company and region. After all, the average salary is a respectable €65,000/year – with everything, absolutely everything, being possible. Also six-figure salaries. 

Since the job description is still quite young, companies are only just beginning to understand the importance of AI for their fields of application. In the near future, ML experts can expect even more lucrative salaries and increasingly exciting tasks. At the same time, they can work in all areas.


CareerKICKstart: Become a teacher for machines

The job description got you hooked? Want to teach machines to learn? Then you might get one of our places in our new Machine Learning Engineering Bootcamp. The further training is designed as a professional specialization and is therefore aimed specifically at people who are already data science pros or have know-how in software engineering. Machine Learning is, so to speak, the "Next Level" or the "Deep Dive" in dealing with software and data. Development basics and a secure handling of SQL are your access card.