Artificial intelligence or AI has penetrated into various fields and is one of the key technologies at the beginning of this century. However, to use these revolutionary tools, you must hire highly qualified experts. Machine learning engineer or ML engineer is one of them.
The job of a machine learning engineer is similar to that of a data scientist. Both can handle large amounts of data, have strong data management skills, and can perform complex modeling on dynamic data sets.
However, the similarities end here. When data scientists generate reports and graphs to show the results of their data analysis, machine learning engineers design and create software that will automatically execute predictive models.
Every time the software performs an operation, it uses the result to perform future operations with increasing precision. Some of the most famous examples are the recommendation algorithms of Netflix, Amazon, TikTok or Spotify. Whenever a user watches a video, searches for a product, or listens to music, the companies mentioned before extract and store data. Machine learning recommendations have become more and more precise without the need for human intervention. The job of the machine learning engineer is to develop such algorithms. In short, the job of a machine learning engineer is a job that requires data science and software engineering skills. In large companies, ML engineers free data scientists from engineering tasks, allowing them to focus on mathematical modeling for instance.
To fully understand the role of a machine learning engineer, it is important to understand what a machine learning project is. It uses software engineering principles combined with data analysis and data science methods to create machine learning models.
For example, ML engineers working for TikTok are responsible for developing the platform's recommendation algorithm. He or she will then have to develop data pipelines and integrate them into TikTok app so that end users can benefit watch relevant videos.
On a daily basis, machine learning engineers will work with data scientists, software engineers, data engineers and UI/UX experts. They also work on internal machine learning platforms and develop new machine learning models.
Usually the data is unstructured, so machine learning must be used. Depending on the nature of the data, machine learrning can be applied in different ways. Machine learning algorithms allow, for example, to recognize images and speech. This will automatically analyze the content of the image and assign a label to it. Similarly, speech-to-text can be used to convert audio speech to text. Therefore, unstructured data can be converted into usable information. Machine learning also allows connections and associations to be established between data. This is very useful in deciphering consumer expectations. For example, a machine learning model can predict that customers who bought product A will like product X. This allows e-commerce companies such as Amazon to recommend products to their customers based on their previous purchases.
Furthermore, machine learning is also used in the financial field to predict risks and prevent fraud through real-time detection. A large amount of historical data can be analyzed to make predictions and determine investment potential or expected payment defaults.