What Skills Should Machine Learning Engineers Have?
Machine Learning Engineers combine data science skills with software development knowledge. They need to strongly understand data science concepts, such as algorithms, math, data structure, probability and statistics, data modeling and evaluation, libraries, programming languages used for data science, and visualizing data. They should also be proficient in software development concepts, like computer architecture, system design, and coding languages, and know how to write clean, maintainable software and systems. Machine Learning Engineers need to have a grasp on their analytical skills for projecting outcomes and troubleshooting issues.
Machine Learning Engineers are expected to know technologies, like Jupyter Notebook, Amazon Web Services (AWS), APIs; version control such as Git; and programming languages, like Python, R, Java, C++, and SQL. Not all of these languages are required for an entry-level position, but it is necessary to know two or more of these languages.
Machine Learning Engineers should have a high-level understanding of how all of these technologies and languages do and don’t work together so that they can make the best technical choices for their specific employer and their business requirements. Additionally, being able to create stable and scalable models is of the utmost importance. MLEs are also often responsible for the distribution of resources within their department, including hardware, data, and personnel.
The world of machine learning is changing both rapidly and dramatically so Machine Learning Engineers must have a willingness and even love of learning in order to keep up with industry evolutions. A keen eye with strong attention to detail also strongly benefits a Machine Learning Engineer. Good communication skills are necessary for this position to explain machine learning concepts to non-technical coworkers and stakeholders. Basic writing skills are a plus for this position, as some machine learning positions require Engineers to publish articles or reports on their work.
Learn the Skills You Need to Become a Machine Learning Engineer
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Data Science
Data science combines domain knowledge, programming skills, mathematics, and statistics to infer crucial insights from data. These insights can be used by businesses, governments, and any other data-collecting entities to inform decisions.
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Python
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. It is used to write scripts, automations, algorithms, manipulate data, and create frameworks. Python prioritizes simplicity, easy to learn syntax, readability, and versatility.
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SQL
SQL stands for Structured Query Language. It is a computer language used to store, manipulate, and retrieve data which is stored in a relational database.
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Machine Learning
Machine learning is the use and study of computer algorithms that improve automatically through experience. It is a subset of artificial intelligence (AI). Machine learning is used in everything from email filtering to Netflix recommendations.
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R Programming
R is a programming language and free integrated development environment (IDE) for statistical computing and graphics. R is most commonly used by Data Scientist and Statisticians for developing statistical software and data analysis.
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Mathematics
Mathematics are used on a day-to-day basis by many technical positions. Subjects like linear algebra, calculus, statistics, and probability are used by Data Scientists, Cybersecurity professionals, Developers, Motion Graphics Designers, Engineers, and more.