How to Become a Machine Learning Engineer
A machine learning engineer is a programmer proficient in building and designing software to automate predictive models. They have a deeper focus on computer science, compared to data scientists.
Machine Learning Engineering has grown in great popularity and is surpassing Data Science. The job title is high in demand with many people from Data Science careers transitioning to become Machine Learning Engineers. It is currently #6 in the top 50 Best Jobs in America, according to glassdoor.
A Machine Learning (ML) Engineer is a programmer proficient in building and designing software to automate predictive models. They have a deeper focus on computer science, in comparison to Data Scientists.
The majority of ML Engineers come from one of two backgrounds. The first is those with a Ph.D. in Data Science, Software Engineering, Computer Science, and/or Artificial Intelligence. The other is people who have prior experience as either a Data Scientist or Software Engineer who has transitioned into the role.
A Data Scientist and ML Engineer both work with dynamic data sets, carry out complex modeling, and have exceptional data management skills.
The main role of an ML engineer is to design software to automate predictive models which help carry out future predictions. This is how the ‘machine’ ‘learns’ from ‘engineering’.
The sub-tasks included in doing this include:
There are a variety of skills required to become an ML Engineer.
You need to have knowledge in multiple programming languages such as C++, Python, and Java with other programming languages such as R and Prolog which have become important elements in Machine Learning. The more programming languages you know, the better; however that can require a lot of studying.
Machine Learning has a heavier focus on computer science, using probability and other statistical tools to help build and validate models. Machine learning algorithms are an extension of statistical modeling procedures therefore having a good understanding of the foundations of statistics and maths is important.
There are going to be times when models fail and it can become very complicated, therefore ML Engineers need to be good problem solvers. Instead of giving up, solving the problem efficiently by understanding the issue at hand and developing these approaches to help you save time and reach your goal faster.
ML Engineers quickly gander through large data sets being able to identify patterns to help them understand what next steps to take to produce meaningful outcomes. Using tools such as Excel, Tableau, and Plotly can also be used to provide greater insight into the data.
Desirable degrees for ML engineers include Mathematics, Data Science, Computer Science, Statistics, and Physics. These degrees provide ML Engineers with the foundations, aswell as skills in programming, statistical tools, and analysis.
