Becoming an Machine Learning Engineer in 2025

Image by Editor | Midjourney

The machine learning landscape is changing and the demand for data scientists, machine learning engineers, software developers, etc is continuously rising, it can be difficult to find a true honest roadmap on how to become a successful AI professional.

The rise of generative AI will have a lot of people considering a role as an AI specialist; however, this focuses on the end product and a unique selling point for it to sell well. If you are considering becoming a machine learning engineer, you have already somewhat decided that you want to be one of the masterminds behind a product or service. And in order to achieve this, you will need to learn the basics. Without the basic knowledge around machine learning, in conversations you will lack substance and people will pick up your lack of education or knowledge.

Here are 3 concise pieces of advice to consider if you’re interested in wading into the machine learning engineering world this year.

Shall I Do Another Course?

 
Courses are great. There are so many different e-learning platforms that provide great resources and content for you to learn and become a successful machine learning engineer. But it gets to a point where how many more machine learning courses do you need to do before you move on to something else that will develop your career?

Start working on projects.

You have learnt the basics, and for you to get from A to B you will need to explore and practise what you have learnt. Without real-life projects, you will never test your knowledge and understand where you may need to improve or what knowledge you have achieved. Machine learning is a huge space and there is a lot for you to learn, therefore practising will allow you to see the connection between all the aspects of machine learning.

This provides you with a high-level understanding of these different aspects, providing you with practical experience in each project you do.

Showcase Your Work

 
Your first project is going to take you a lot of time and when you look back at it you’re going to laugh at the mistakes you were making, big or small. That is completely normal because the whole point of project-based machine learning tasks is that they take you through a journey of constantly improving. This improvement in your skills is key to your machine learning journey.

You need to showcase your work.

Showcasing your work can come in many forms, be that in the form of a blog post, a video, or a LinkedIn post. This will allow you to show off your skills as well as receive feedback on areas of improvement. But also remember all the steps you had to take in a single project can help somebody else on their machine learning journey.

Stay Up to Date

 
Every month there are thousands of machine learning papers being published and new tools being created, which can be very overwhelming to constantly keep up with. The key thing to remember is: focus on what it is you want to do, and assess the importance of new tools and techniques.

Cut out the noise, focus on the signal.

For example, your job may require you to be proficient in Pandas, but a new tool has come out that has got you interested. I am not saying don’t learn the new tool, but understand you don’t have to put your all into it if it will not serve you.

When new tools and techniques come out, spend some of your time learning these without compromising your job or other career goals. The best way to test your new skills is to go back to projects and put your skills to the test.

Wrapping Up

 
You won’t achieve any of this overnight. This will take time and it’s not to discourage you from your goals, but to encourage you to take your time and make sure you do projects to constantly improve yourself. You will fail and learn, then fail and learn, and then fail and learn again. This is the process of learning something new.

Don’t be scared, and get started now!
 
 

Nisha Arya is a data scientist, freelance technical writer, and an editor and community manager for KDnuggets. She is particularly interested in providing data science career advice or tutorials and theory-based knowledge around data science. Nisha covers a wide range of topics and wishes to explore the different ways artificial intelligence can benefit the longevity of human life. A keen learner, Nisha seeks to broaden her tech knowledge and writing skills, while helping guide others.