Machine learning is flourishing as a big industry. If you are interested in this field, join it! As a machine learning engineer, I can guarantee that it will keep you engaged every single day.
But how to become a machine learning (ML) engineer? Well, this career journey will be tough, but you can pull it off with your passion and commitment. I will share my experience on this path now. So let’s see!
Overview
Machine learning is a subcategory of AI (Artificial Intelligence) and computer science. It focuses on using algorithms to imitate humans. If you want to dig into this field, let me help you get some basic information about it first.
Who is a Machine Learning Engineer?
A machine learning engineer is a programmer who builds software for that human-like task. They create systems to teach the computer smart tricks.
ML engineers also manage the data pipeline. For example, they source and prepare data to build and train models. This way, the models can act smartly.
What Education Does a ML Engineer Need?
You should start by getting a bachelor’s degree. It’s even cooler to get certificates showing you are good at machine learning.
But education is not the only thing to work on. Most managers care more about your experience. So, you can consider joining training camps and internships to hone your skills.
As you work, you may want to get a master’s degree. Yet, it’s optional. And you should check the requirements of the company you want to work for first.
Work Environment
You have two options for where you work. Firstly, you can be at the office. If you choose a nine-to-five job, you will work around 40 hours each week.
You can also work from home. This solution gives you a more flexible schedule. But, of course, you have to remain available whenever needed.
What Do Machine Learning Engineers Do?
As a machine learning engineer, I have exciting work days. Here is what I do every day:
- Building algorithms: Algorithms are steps for computers to learn from data. They then help the computers predict things based on what they’ve learned from the past.
- Checking ML processes: After building algorithms for the models, I make sure they are doing their jobs well. So, I check their working process and adjust when needed.
- Analyzing statistics: There is unstructured data in the models. Thus, as an ML engineer, I work on it and turn it into something useful.
- Testing: The AI must work as planned. To ensure its smooth performance, I test it carefully.
- Fixing programming bugs: I also find bugs and fix them. Then, the program can work better and learn more accurately.
- Documenting: After finishing the development process, I write down the steps. I will explain how I made the program, too. This way, my team and bosses know everything about it.
How To Become A Machine Learning Engineer
I have taken a long route to become an ML engineer. But I will divide it into six simple steps to help you easily picture what you need to do.
1. Learn How To Code
Start your journey with coding! The main programming languages to focus on first are C++ and Python.
Python is the most popular option for machine learning. It offers a large community of developers and a wide library. Thus, your learning path is easier.
Meanwhile, C++ helps you work with memory management easier. Its high-performance computing is also useful.
Next, work with SQL to handle big data. Then, try Github to share your work with others in the industry.
You can use toolboxes, too. I like TensorFlow, as it works with different coding languages and helps computers understand things easily.
Another option is PyTorch. This toolbox assists you in creating smart systems. It’s also a friendlier choice for beginners.
2. Enroll In A ML Course
The coding basics are not enough for your job. But don’t worry! Online courses can help you improve your coding skills.
The course offered by BrainStation is a good idea. This course teaches you how to use ML for real-world cases. For example, you can build smart systems to solve business problems.
3. Try A Personal Machine Learning Project
At the end of the course, try a project. It’s where you put your knowledge into practice and test your skills.
You can also ask your friends to join. Then, you all will learn how to use different frameworks. Exchanging ideas also helps create an impressive project.
Once the project is done, share it online. Include the project in your portfolio, too. When you apply for an ML engineer, show it to the employers. A sample of your previous work can demonstrate your skills and impress them.
4. Learn How To Work With Data
When you are creating AI programs, they will handle lots of information. For example, if you build software for marketing, it must work with the marketing statistics and market research info.
As you can see, data is essential in machine learning. Your machine should be able to do data-heavy tasks easily and accurately. So, how can you overcome this challenge? You can start with cloud platforms. They let you store data safely.
Virtual machines can also help. They work super fast with AI stuff. Even big companies like Microsoft and Amazon need them. They have those machines ready for you to use, too.
The key to working with data is to find automated systems and a secure platform. Once you can get both, the learning process of your AI programs will be really efficient and fast.
5. Join Online ML Communities
You should also make new friends who have the same interest. I’m a member of Kaggle, where I can meet professionals in machine learning and computers.
At Kaggle, you can share cool things you’ve discovered and learn from others. This community encourages everyone to interact and help each other grow.
You can showcase your projects there. Then, the feedback from your pals will help you improve your skills. Plus, by joining Kaggle, you will learn a lot from others. It’s also an excellent chance to try your best in challenges.
6. Apply To ML Internships and Jobs
You’ve learned a lot and done many projects. Now, it’s time for the real thing. You can search for internships first and apply for an ML job.
During the internship, you can hone skills you can’t get from books and classes. You will also meet people who work in your field.
Moreover, internships are valuable stamps on your resume. They show future employers that you’ve actually done real projects.
What’s more, an internship can lead to a full-time job offer. If you show that you are a great fit, the company may want to keep you on their time.
And if not, no worries! You can still look for job opportunities in other companies. There are many online job platforms for machine learning engineer positions. So apply to the ones that match your interests and skills.
Job hunting takes time. Thus, don’t give up if you can’t find a job right away. Trust me; persistence pays off.
Here are some tips for applying to a machine learning engineer job as follows:
- Read the job description carefully to understand the skills the companies seek.
- Then, highlight skills and experiences in your resume so it can match the job description. Remember to share your machine learning projects on the portfolio, too.
- Write a brief cover letter to explain why you are well-suited for the position.
- Customize your resume for each job to show your interest in that specific role.
- Be honest about what you can do. If you exaggerate your skills, the interviewers may figure it out.
Salary and Job Outlook
As of 2023, the average salary for machine learning engineers is really high. You can earn about $127,448 per year. This salary range is a little higher than the national average rate.
There’s more to expect about this career! In 2019, machine learning engineering was the most popular job based on the number of job openings. But what is the reason behind its popularity?
We collect tons of data from the internet and devices. So, how can we handle it all? That’s when machine learning comes into play.
Essential Skills
To become a good machine learning engineer, you need many skills. The most important ones are:
You will work with a lot of numbers. Surprisingly, a machine learning engineer needs skills similar to a data scientist. Thus, you should learn data patterns and programming languages like Java and Python.
After that, practice your testing skills. You have to judge if the predictions from your models and algorithms are correct.
As a machine learning engineer, you must be proficient in computer science concepts. They include algorithms, data structures, and computer architecture.
Moreover, since you have to create computer programs, you should also know how to work with software. There are many parts to cover, such as testing, system design, and requirement analysis.
Machine learning is the combination of software engineering and data science. However, remember that you are still a machine learning engineer. So, try to dive deeper into this concept.
Machine learning is just a part of AI. Even so, you need to study many things, such as neural networks, dynamic programming, and deep learning.
Technical skills are essential, but you still need to develop your soft skills.
- Communication: You will talk with both technical and non-technical people. So, clear communication is crucial for a smooth working process.
- Problem-solving: There can be many problems when developing ML programs. In such cases, you must find creative solutions to handle them.
- Time management: You have many tasks to do, like coding and testing. So, good time management can help you keep things on track.
- Attention to detail: Every detail in your code counts. Thus, you must pay attention to each. Otherwise, the whole system will break down.
Is Machine Learning A Good Career?
Yes. If you choose this career path, you will get many benefits, such as:
As I have mentioned, this job is well-paid. Moreover, the demand for it is quite high. It means you will have many job opportunities.
When working as a machine learning engineer, you have chances to try new technologies. So, if you love coding, you will enjoy discovering new things for innovative apps.
Machine learning is also an excellent choice for math lovers. You will work with calculus, statistics, and probability. It’s exciting to do what you love, right?
Believe it or not, you will never get bored when taking this role! This field is quite new, and there are many algorithms and tools to explore. Hence, you can upgrade yourself almost every day.
I often join workshops to check what’s new in the industry. So you can try them! They will help expand your network and connect with your pals.
Machine learning can serve almost any industry, such as healthcare, marketing, or cyber security. So feel free to choose your favorite sector.
Conclusion
There are only six steps to becoming a machine learning engineer. Yet, it takes years to finish. Even when you are working in this position, you still need to keep working to improve every day.
So, take those first steps! This journey is exciting and rewarding. Just let your passion for technology guide you.
Thank you for reading!