Many of you have asked me what a better career option either machine learning or software engineering?. When I was doing my Masters in Computer Science, I had exactly the same dilemma, and so did a lot of my classmates. Both careers are lucrative, both are worthwhile, but they are completely different.
I’ve been in the industry for a while now and hope to have some useful ideas to come up with. In this article, we’re going to compare machine learning and software engineering careers considering the following factors:
1. Salary and Growth of Vacancies
2. Barriers To Entry
3. Your Dispositions
4. Predictions For the Next 10 Years
If you’re ready, let’s get started, money matters, and a lot of people are drawn to technology because of the money, and they’re probably isn’t anything wrong with that. So let’s take a look at the machine learning engineer salary breakdown compared to software developers in the United States (US) and the United Kingdom (UK).
First, let’s take a look at the US market. On average, software developers make 92,000 a year, compared to $ 114,000 for machine learning engineers. In the UK, too, the situation looks pretty similar to the salary of software engineers on average – £43,000 average compared to £47,000 for machine learning engineers. This data comes from Glassdoor and obviously doesn’t take into account the geographical breakdown in each country.
For example, if you work as a software developer or machine learning engineer in Silicon Valley, you will obviously earn a lot more than I just quoted. This is just an average and the same for London in the case of the UK. The point is, as you can see in both countries, machine learning engineers make marginally more money than software engineers, but what about the demand for machine learning engineers versus software analytics engineers? on the number of open positions as reported by Indeed.com.
According to Indeed.com, the number of machine learning engineer jobs increased by 344 between 2015 and 2018. Can you imagine that and the number of jobs in software engineering has increased by 207 over the same period? Notable growth in both areas, but again, the number of machine learning engineer positions has increased slightly. So when it comes to the financial aspect, machine learning engineers seem to win on the second aspect.
Let’s look at your dispositions. Now I think that this is the most important factor in the whole decision-making process, and by that, I mean that machine learning and software engineering are completely different areas. They require completely different skills and you need to have a completely different mindset to solve machine learning problems versus software development problems. Now, of course, you’re more suited to machine learning or software engineering.
Let’s compare and see what machine learning is. Machine learning is a time of statistics. Statistics mean math; If you’re not good at math, about to forget about machine learning, and I’m not saying you can’t learn it because if you try hard enough, you probably can, I am saying that you will find it extremely difficult and will what it is about. So machine learning is for someone who loves complex math puzzles and thinks abstractly theoretically.
What is software development? Well, software engineering is a type of engineering and that means putting things together to make them work. It’s a much more practical area; The most important thing is your creativity. Practicality: It is not important that you are familiar with math as it is usually not really involved in the process. A distinction is therefore made between machine learning and software engineering.
Another difference is that in software development you get this instant feedback from the system. So you do something, you code your solution, and it either works or it doesn’t. Basically, you get that feedback and you know you did something right. When you’ve done something right and that makes you happy, while in machine learning, there are so many arbitrary things that happen in machine learning. If you get very murky results, then you may not have cleaned up your data well, or the data is inherently messy and it isn’t much you can do about it, or you have initialized your hyperparameters to wrong values, or this algorithm may not the best is you adjust the dates and there may be a number of things you know and just don’t know and you never know for sure what it is and you don’t know if you are doing something right or wrong. It doesn’t give you that instant satisfaction, instant feedback.
So, you know that being a software developer is an infinite learning curve. Can a curve have no end? It’s just that there’s a long bend, there’s no horizon, oh one second my bundle decided to die, I can’t blame it; It is so difficult that if you are better suited to machine learning or software engineering, it depends on whether you love complex math, then machine learning is for you or you love to make construction materials then software engineering is in Generally an attraction for you.
The third consideration concerns the barriers to entry for machine learning. In job advertisements, the entry barriers are very often very high. You will see that an advanced degree in math, statistics, computer science, or other quantitative degrees is required, which sometimes even explicitly asks for a doctorate. in these areas. Well, that’s not always the case and I’ve even written a separate blog post on how to get a machine learning job without a special degree in these quantitative areas. However, it often happens that this degree is at least useful and can be contrasted with software engineering.
Sometimes open positions require a degree, but it’s rarely an advanced degree like masters or doctorate, and even if you don’t have a bachelor’s degree, it is often enough to have a portfolio of projects that you have coded, and for which you basically have evidence that you can code, so in that category, I think software engineering wins.
Let’s talk about the predictions for the next 10 years. Unsurprisingly, machine learning is still on the rise due to the huge amount of data we produce every day. Every day 500 million tweets are sent, four petabytes of data are created on Facebook, 294 billion emails are sent, four terabytes of data are created, 65 billion messages are sent on WhatsApp in every connected car, five billion searches, so you generate a lot Data, and someone needs to extract knowledge from the data for governments. For businesses, and I also think that many machine learning engineers in particular in smaller businesses need to learn data engineering skills as data engineering skills will be extremely important.
Software engineering isn’t going anywhere for the next 10 years either. The trend, however, is that we will program less and less, as it is now and will undoubtedly be even more important in the future. Basically, software engineering is about putting things together, understanding the ecosystem, how the different applications communicate with each other, and you know what I enjoy. In fact, I’m looking forward to this change because I think it will unleash creativity. Personally, I prefer this aspect of building rather than finding the most efficient solution. They may be different, but I value efficiency, but not to the point where it basically slows you down and is no longer efficient as I haven’t spent days trying to come up with that perfect algorithm.
I just want to keep going. Well, here at Draw, too, both software engineering and machine learning will be in demand in the future. So we have a tie between the two races to choose the right one for you now. Ask yourself these questions, do you prefer math puzzles or do you build things? Do you have a degree or can you afford one or not? because a degree could be a requirement for many machine learning jobs, doesn’t it have to be? Answering these questions can help you decide between the two careers.
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