Through a combination of software and Artificial Intelligence (AI) comes the machine learning industry. It’s a part of the tech industry that focuses on making things easier on a technical level through prediction and calculation. For example, Netflix uses machine learning in its Recommended For You section. Though the electronic elements are what businesses use, don’t forget that it was made by humans.
One such job in that area is machine learning engineer or MLE.
What are MLEs?
These engineers are well-versed in software engineering on top of machine learning. Rather than constructing machine learning (ML) algorithms, MLEs work with the engineering side of things to help businesses produce their products. Data divisions are where MLEs are typically found—among data and business analysts, data engineers, data scientists, and more.
MLEs vs Data Scientists & Engineers
Data scientists use research and conduct data experiments. On the other hand, data engineers are the ones who gather data from the original source and keeps a record of it. MLEs take everything that has been passed through data engineers and data scientists then polishes it before sending it to consumers.
What MLEs Do
MLEs serve as a bridge of sorts between frontend and backend development to help with the creation of apps with AI capabilities. They also make using ML procedures easier by establishing a machine learning pipeline that is also scalable. There are times when MLEs must also write custom code, depending on the scope and tasks of the project.
With apps that are centered on data enterprises, MLEs take on a role with automating the production of models used, from beginning to end—known as MLOps. Additionally, MLEs enhance and analyze machine learning algorithms. This is generally what MLEs spend most of their time on by making sure they understand the company’s goals, create tests to run against the models they make, and alter models to improve them.
Skills MLEs Need
Math is a large portion of an MLE’s skillset. Basic algorithms, algebra, statistics, and probability are all areas they should have an understanding of. Some form of experience in programming is necessary to be an MLE as well. They need to be knowledgeable of what programs are popular and how to use them. These programs may include Python, Java, and C++ but can also extend to others such as Prolog, Lisp, and R. Competent communication and good problem-solving skills should be the bare minimum of an MLE’s skills.
There are many uses for MLEs, even though the job is considered to be a relatively new addition to the tech industry. This career balances the line between math and science since it requires both as well. Additionally, MLEs are a part of both the data and AI industries—two of the most popular inside the much broader tech industry. As with any project or job in this industry, the level of skills and experience needed will vary. Businesses may be lenient with their qualifications or the depth of the tasks may change. MLEs should be flexible and adaptable for the best chance of being successful in a difficult industry.