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Machine learning (ML) is a division of artificial intelligence that focuses on programming systems to be able to learn from experiences and adapt. With the help of machine learning, computer programs can access data and react to it, adapting future behaviour without being explicitly programmed to do so.

A machine learning engineer has a similar role to that of a data analyst. They must both analyse and present data that impacts decision making. However, in the case of machine learning, engineers produce software that is capable of integrating the obtained data and making decisions itself.

In more detail

A machine learning engineer’s role and tasks may vary drastically depending on the company and domain they work in. All their software and machine learning algorithms are therefore adapted to the goals of the company or product.

As such, they must have an understanding of the business, how it works, and what it is trying to achieve. Then they can decide where to implement machine learning algorithms. It is necessary to have experience in both software engineering and in data science.

Machine learning engineers are responsible for data collection and processing in order to identify opportunities for improvement. Subsequently, they must research and build machine learning models in order to solve problems or optimise processes.

The models require evaluation and maintenance, which are also the ML engineer’s responsibility. Throughout the process, they must document their progress and present the solutions to the teams that will then work with them.

Machine learning engineers must first and foremost be good engineers. This means producing good designs, ensuring proper testing of the algorithms, and providing clear documentation.

A background in software engineering is necessary, and companies generally seek candidates with at least a master’s degree in Computer Science. It is especially good to specialise in Machine Learning or Artificial Intelligence.

A good understanding of statistics is essential, as is knowing how and when to use different machine learning models. The necessary skill stack includes Python, R, Tensorflow, Pytorch, and MATLAB, among others.

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