How to become a Machine Learning Engineer?
The title ‘Machine Learning Engineer’ has become very popular over the years. Many people enter Data Science and transition to become Machine Learning Engineers. Others are highly educated and experienced and have the right skills. Let’s first start with the definition of a Machine Learning Engineer.
A Machine Learning (ML) Engineer is a programmer proficient in researching, building, and designing software to automate predictive models. Their role is to build Artificial Intelligence (AI) systems that consume large amounts of data to generate and develop algorithms capable of learning and making future predictions.
Machine Learning is a form of Artificial intelligence that allows models to learn and improve user experience by exploring the data and identifying patterns with little human intervention.
The majority of this day and age ML Engineers come from two backgrounds. One is highly educated with higher qualifications in Data Science, Software Engineering, Computer Science, and Artificial Intelligence. The others have more experience in being a developer, from Data Scientists, Software Engineers, or Data Engineers. They have transitioned into the role by building their skills and interests.
From this, we can tell that becoming an ML Engineer consists of being highly educated, having years of experience, and a level of proficiency.
What does an ML engineer do?
A Data Scientist and an ML Engineer share similar duties. Both roles work with dynamic data sets, carry out complex modeling, and have exceptional data management skills.
Data Scientists produce outputs through data visualizations and reports presented to others to give a better insight. On the other hand, ML Engineers design software to automate predictive models, which help carry out future predictions, increasing the accuracy each time the software is run. This is how the ‘machine’ essentially ‘learns.’
An example of Machine Learning implemented is recommendation algorithms used on most platforms such as Netflix, social media, and Amazon. For example, when a user watches a Netflix series from the thriller category, more data is added to the algorithm. As the data grows, the algorithm recommendation to the user becomes more accurate.
Four steps to becoming an ML Engineer
1. Undergraduate Degree
Mathematics, Data Science, Computer Science, Statistics, and Physics are desirable degrees to become an ML Engineer. These disciplines provide the foundation for aspiring ML Engineers, providing knowledge and skills in programming, statistical tools, and analysis.
2. Junior role/Initial career
Unfortunately, you will not be able to become an ML Engineer through an entry-level position. You must build your skills and experience in Software Engineering/Programmer/Developer, Data Scientist, and Computer Engineer. Once your level of proficiency in programming increases, you will be able to transition into becoming an ML Engineer.
3. Masters or Ph.D.
Although it is possible to become an ML Engineer with an undergraduate degree, having a Master’s degree in Data Science, Software Engineering, or a Ph.D. in Machine Learning will provide more opportunities and help you thrive as an ML Engineer.
4. Never stop learning
The technology industry is growing rapidly; therefore, keeping up to date with changes and new advances is critical. Being on top of understanding new algorithms, machine learning platforms, and libraries will always keep you ahead of the game.
In addition, educating yourself with online courses and professional certificates and surrounding yourself with other ML professionals will put you in a better position to develop your ML Engineer career.
What are the typical tasks assigned to ML Engineers?
Although tasks assigned can vary, depending on organization size and the data team’s role concerning the companies’ goals, most ML Engineer roles include:
- Designing and developing systems, models, and schemes
- Collating appropriate data sets
- Performing statistical analysis to improve models
- Iterating training on ML systems and models
- Visualizing data for companies’ insights
- Applying research and findings to business decisions
- Improving existing ML frameworks and libraries
- Ensuring data quality
- Experience working in an Agile environment
- Advanced math skills (linear algebra, Bayesian statistics, group theory
What skills do you need to possess to be a successful ML Engineer?
There are a variety of skills required to become an ML Engineer. Below is a breakdown of the hard skills most found in successful ML Engineers.
1. Programming Skills
To be a successful ML Engineer or have a career in Artificial Intelligence, knowing multiple programming languages such as C++, Python, and Java is important.
In addition, other programming languages, such as R and Prolog, have become essential elements in Machine Learning—the more programming languages you know, the better. I’m not saying you have to be an expert in all of them, but see them as ingredients to your recipe. They all work well together.
2. Statistics
Many Machine Learning algorithms use probability and other statistical tools to help build and validate models from observed data. Furthermore, many machine learning algorithms are extensions of statistical modeling procedures and are used widely; therefore, understanding the foundations of statistics and mathamatics is important to being a successful ML engineer.
3. Problem Solvers
Machine Learning Engineers need persistence; there will be times when models of experiments fail. Instead of giving up, curiosity will be your drive to help you solve the problem.
It’s not about sitting there banging your head, trying out 1000 different ways till you figure it out. Solving the problem efficiently by understanding the issues and developing approaches to fix bugs saves you time, allowing you to build your model with ease.
4. It’s an Iterative process
Iteration is the repetition of a process to generate a specific outcome. To be an efficient ML Engineer, the love and enjoyment of building a simple model, to begin with, and then iterating on improving it with each stage is normal. However, understanding when iterating over the same model, with little improvement, is no longer worth the time is when the problem-solving skills help.
5. Understand Data
A good ML Engineer can quickly gander through large data sets, identify correlations/patterns, and know which next steps to take to produce meaningful outcomes. Database visualization tools such as Excel, Tableau, and Plotly also come in handy to ensure others can clearly distinguish your insights.
Start your ML Engineering Journey
With determination, you will be able to become a successful ML Engineer. Of course, it 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.
According to Glassdoor, Machine Learning Engineer is currently #17 in the top 50 Best Jobs in America, including Java Developer at #1, Data Scientist at #2, and Software Engineer at #9. The world of technology has been evolving and will continue to evolve.
Nisha Arya is a Data Scientist and Technical writer from London.
Having worked in the world of Data Science, she is particularly interested in providing Data Science career advice or tutorials and theory-based knowledge around Data Science. She is a keen learner seeking to broaden her tech knowledge and writing skills while helping guide others.