Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way humans learn, gradually improving accuracy. Machine learning jobs are common in many technology fields, so the landscape of specialized roles is rapidly evolving. If more people learn machine learning at least a little, it could eventually become a common skill set for all software engineers. This is the most important reason why machine learning engineers should change jobs in 2023 and try some new skill sets to secure their careers. There are multiple reasons not to become a machine learning engineer, but the number one reason is that machine learning is rather not easy to master. And machine learning engineers should change jobs because they are not useful in this economy. In this article, we will focus on 10 reasons why machine learning engineers should change jobs in 2023.
Machine learning takes time and resources to produce tangible results
Machine learning progresses over time, so there will be a period of time when the interfaces and algorithms are not adequate for your company's needs. The exact length of time required will depend on the nature of the data, the data source, and how you use the data. You may simply have to wait until new data is generated. In some cases, this could take days, weeks, months, or even years.
Machine learning will become commonplace
Machine learning will move into becoming a common part of every software engineer’s toolkit.
The role of Machine Learning Engineer is the result of massive hype fueling buzzwords like AI and data science in the enterprise. In the early days of machine learning, it was a much-needed role. And for many, it led to a small salary increase. But the Machine Learning Engineer has taken on many different personalities depending on who you ask. Currently, top technology companies don't have a clear understanding of what a Machine Learning Engineer means for their company. This can leave machine learning professionals in the dark.
Machine learning engineers are needed now
Machine learning engineers will be needed as long as understanding machine learning is rare and the barrier to entry is high. As we all know, the role of machine learning engineer will be completely taken over by general software engineers. Engineers will move into a standard engineering role of taking a specification or reference implementation from someone upstream, translating it into production code, and shipping and extending the application. For now, many machine learning roles exist in a strange space of working on ML problems that have never been addressed before. Soon, most companies will require very little research effort to complete a project. Only niche use cases and deep technical efforts will require special skill sets. Therefore, pursuing your passion in this field is very risky.
I need to stay up to date
As mentioned earlier, machine learning is a rapidly evolving field. Due to this, machine learning engineers will need to devote a significant amount of time to learning the latest developments in the field. Reading and learning from research papers from various universities and organizations will need to be a part of your daily routine if you want to pursue this field. Hence, unless the idea of continuous learning appeals to you, you should reconsider your decision to become a machine learning engineer.
Tough work
Training models, crunching data, building and testing prototypes on a daily basis can be mentally exhausting. For machine learning engineers, data mangling is also a painful part of the job. Data mangling means converting raw, unprocessed data into a better, more usable format. You might have to scrape data from paginated websites and integrate it with your client's internal data, while dealing with datetime and data type errors. This is not an easy task and can be stressful for some.
Finding a mentor seems difficult in machine learning
Many internet influencers preach that it is really easy to get started with machine learning. All you need to do is download the Titanic dataset and copy 10 lines of Python code from a tutorial to get started with machine learning. It may seem easy here, but it gets harder as you go deeper. Having a good mentor is very important so that you don't have to figure everything out by yourself. Getting a good internship is also a great way to grow as an engineer. It is quite hard to find a good mentor, but you can find one if you research.
Machine learning jobs are hard to get
Finding a job as a machine learning engineer is harder than finding a job as a front-end (back-end or mobile) engineer. Small startups usually can't afford to hire a machine learning engineer. And since they're just starting out, they don't have any data. You know what they need? Front-end, back-end, and mobile engineers to launch their business.
Rising wages
Senior machine learning engineers don't earn more than other senior engineers. There are some machine learning superstars in the US, but they were in the right place at the right time. They had the right mindset. There are software engineers in the US who earn even more.
Machine learning is the future
Machine learning is here to stay, and the same is true for front-end, back-end, and mobile development. If you work as a front-end developer and are happy with your job, keep doing it. If you need to create a website using machine learning models, partner with someone who already has the knowledge.
Machine learning is fun. Is it true?
Machine learning is fun, but not always. Many people think they will be working on artificial intelligence or self-driving cars. But they are more likely to be working on creating training sets and developing infrastructure. In reality, ML engineers spend most of their time figuring out how to properly extract training sets that resemble real-world problem distributions. Once you do that, you can train a traditional machine learning model, and it will work well enough in most cases.
Conclusion
The purpose of this article is to give you a critical perspective that you don't usually hear from influencers. I don't mean to discourage you. If you feel like machine learning is for you, then by all means give it a try. However, machine learning is not for everyone, and not everyone needs to know about it. If you're a successful software engineer and enjoy your work, keep going. A basic machine learning tutorial won't help you advance in your career.
