The two hottest jobs on the market right now are full-stack developers and machine learning engineers.
And it’s for a good reason too.
These roles take immense skill and problem-solving abilities that only some are equipped with.
While the skills they require are similar, there are some huge differences between these roles.
And besides, which is the better role to have in today’s tech industry?
This blog post will discuss each role – in case you’re on the fence to help you decide which one is right for you!
What Does A Full Stack Developer Do?
A full-stack developer is a tech expert who works with front-end and back-end coding.
They are true masters of development – tackling problems, creating solutions, and being an all-around knowledge powerhouse!
Full-stack developers usually act as project leads, as their role is a perfect segway into leadership – as they understand the full scope of a project.
Since these tech gurus understand multiple programming languages, they can:
- Identify potential weak spots in software
- Develop creative and fast solutions
- Set reliable user interface standards on websites and apps
- Debug programs quickly when errors occur
- Scope new features with members of the team
And still have time to teach a junior dev their tricks.
It takes a true innovator to be a full-stack developer – someone who can abstract problems past the limits of one language or framework and create comprehensive and scalable solutions.
Full-stack developers constantly seek the latest trends and knowledge to stay ahead of the curve.
Their job literally depends on it.
What Does A Machine Learning Engineer Do?
A Machine Learning Engineers’ primary responsibility is to create and maintain Artificial Intelligence (AI) systems.
This means they’ll need a combination of technical expertise and creative problem-solving skills to develop intelligent machines that can learn, adapt, and perform various tasks autonomously.
Machine learning engineers use algorithms, statistics, computing science, technology, and existing data sets to craft the best algorithm for their business needs.
Once they’ve decided they have a good model, they’ll deploy these algorithms into production systems so that these models can continue to solve problems.
Machine learning engineers do tons of DevOps (called MLOps), keeping the servers that host these models and pipelines alive.
It’s a challenging but rewarding role – no two days are the same!
Main Difference Between A Full Stack Developer And A Machine Learning Engineer
The main difference between a Full stack developer and a machine learning engineer is the machine learning aspect.
I often do some full-stack development in my day-to-day job (I’m a MLE).
However, you’ll never see a full-stack developer doing any machine learning.
So, while I spend a lot of my time cleaning datasets, managing pipelines, and messing around with production servers, a full-stack developer will spend more time adding features to applications and enhancing the front-end user experience.
Should Full Stack Developers Move Into Machine Learning?
Full-stack developers would make great machine-learning engineers.
Since they already know DevOps and other computing basics, they’ll only need to pick up some data science and modeling techniques to get up to speed quickly.
Which Is Faster To Learn, Machine Learning Or Full Stack Development?
With the explosion of coding boot camps, I decided to let the boot camps tell us this answer.
According to my research, there’s a 14 to 1 google search trend in favor of full stack developer Bootcamp compared to machine learning engineer boot camp.
While these two were similar in search volume before 2010, the full stack position has been the go-to for newcomers since then.
This makes a lot of sense since there’s an immense amount of math that you need to know to become a machine learning engineer – it’s faster to learn to code.
Full Stack Developer vs. Machine Learning Salary
While it may be faster to learn full-stack development, you will be missing out on about 50% of the pay.
According to glassdoor, machine learning engineers make, on average, $130,000 a year, compared to $90,000 yearly for full-stack developers.
While a machine learning engineer’s salary is obviously more, this shouldn’t take away from how great it can be to be a dev.
Is There A Role That Is A Blend of Full Stack Development and Machine Learning?
There is a blend of full-stack dev and machine learning engineering… it’s called a machine learning engineer.
When most companies hire a machine learning engineer, they assume you understand and know most of the coding that regular devs have to do.
For example, most top tech companies give the same coding tests to their software engineers as they do to their machine learning engineers.
Any role that sits outside of research will have a dev aspect, where you build and configure systems to serve customer needs.
These roles, just by their nature, are full stack + machine learning.
This makes sense if we go back to the salary data above; for machine learning engineers to get paid so much, they need to know how to code like a dev – and model like a machine learning engineer.
Other This Or That Articles
We’ve written a couple of other articles that are very similar to this one; check out:
- Machine Learning vs. Automation
- Machine Learning vs. Human Learning
- Data Science vs. Economics
- Machine Learning Vs Programming
- Data Science vs. Bioinformatics
- Data Science vs. Operations Research
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