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.
Image by rawpixel
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.
What Does an ML Engineer Do?
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:
- Researching ML algorithms and tools and how they can be implemented.
- Selecting the appropriate data sets
- Selecting data representation methods
- Verifying the quality of the data
- Identifying the distribution in the data and how it affects model performance.
- Iterating training on ML systems and models
- Perform statistical analysis
- Fine-tuning the model
- Improving existing ML frameworks and libraries
What Skills Do You Need To Be A Successful ML Engineer?
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.
How To Start Your Career as an ML Engineer
David Iskander via Unsplash
Traditional route: University
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.
If you would like to get a better insight on the type of content you will learn at University, have a read of this article: Free University Data Science Resources.
Once you have completed a degree, you will need to build your skills and experience in fields such as Software Engineering, Data Scientist, etc. ML Engineers require a few years of experience with a high level of proficiency in programming to be successful.
You can further increase your knowledge by getting a Master's degree in Data Science, Software Engineering, and/or a Ph.D. in Machine Learning.
Modern tech route: e-Learning
With the demand for tech experts in this day and age, another possibility is independent and/or e-learning. This can be done through BootCamps, online courses, Youtube, and more.
If you are looking to learn through YouTube, there are a variety of YouTube channels that can help you get there. There are YouTubers such as John Starmer, Krish Naik, and more. If you would like to know more, have a read of this article: Top YouTube Channels for Learning Data Science.
There are also a variety of online courses, some of which are provided by Universities. This shows the demand for tech experts as Universities have taken the time to create courses to help meet this demand. With the new remote lifestyle, online courses are becoming more and more popular to help accelerate people's careers.
An excellent platform that has recently interested me is Great Learning, which provides courses in Data Science & Business Analytics, AI & Machine Learning, Cloud Computing, Software Development, and more. One of their most popular Machine Learning courses is: Data Science and Machine Learning: Making Data-Driven Decisions Program.
ML Engineers have to know a lot of knowledge surrounding Machine Learning, and the different types of algorithms. If you would like to know more about the type of algorithms you will learn in Machine Learning, have a read of this article: Popular Machine Learning Algorithms.
Although many things have moved online, fewer and fewer people read books. Books are a great way to learn, however, it can be difficult to know which book to choose. I would highly recommend the book Machine Learning for Absolute Beginners by Oliver Theobald.
If you would like more Machine Learning book recommendations for different levels of learning; beginners, intermediate, and experts, have a read of this article: Machine Learning Books You Need To Read In 2022
It’s Not An Easy Route, But It’s Worth It
Becoming an ML Engineer won’t happen overnight, but once you have obtained the correct qualifications, skills, and experience, you will be in a field that provides you with a solid future. It requires a lot of hard work and determination, all you need to do is put in the work.
Nisha Arya is a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.