Asking the question of how to become a machine learning engineer? Or thinking to start a career in machine learning?
Well, I am going to discuss everything in detail on how to become a machine learning engineer.
If you love challenges and willing to solve complex problems through machine learning algorithms and libraries, then a career in machine learning may be an excellent option for you.
Moreover, machine learning jobs come with a fat paycheck.
According to Indeed.com, the average salary for machine learning engineers was between $126,648 and $162,740 in 2024.
Not only that, but the World Economic Forum’s (WEF) jobs report shows that there will be 133 million new jobs tas a result of AI-enabled technologies by the year 2022.
So, let’s see and understand what are the skills, requirements, steps, and qualities that you need on how to become a machine learning engineer.
What is Machine Learning?
Before going forward with how to become a machine learning engineer, let’s start by understanding what defines machine learning.
Machine learning is the area of study or practice that allows computers or machines to learn from observational data and develop predictions based on that.
Technically, machine learning algorithms allow computers to learn without the need to be explicitly programmed by us.
Here’s a bit more general definition of machine learning:
“Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed” – Arthur Samuel, 1959.
And for all you engineers out there:
“A computer program is said to learn from experience E concerning some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.” – Tom Mitchell, 1997.
Machine learning models learn from past data to make predictions on future data.
Related: What is Machine Learning? – Supervised & Unsupervised
A spam filter is an excellent example of machine learning. It automatically learns how to flag spam emails when you feed the system with samples of spam emails and regular emails.
A key concept to remember is that machine learning programs learn to make predictions by finding patterns through vast amounts of data.
So, what differentiates machine learning program with the traditional programs?
The critical difference is uncertainty. I mean that machine learning programs learn to deal with changes and probabilities, unlike regular programs.
You also have to familiarize yourself with concepts such as data structures and algorithms.
Remember the coding process of a machine learning program is entirely different from coding a regular application.
Defining a Machine Learning Engineer
A machine learning engineer is responsible for building solutions that utilize machine learning algorithms and processes.
We can define an ml engineer as an individual who develops software or models that learn from data and makes predictions.
As a result of the model that an ml engineer comes with, these predictions can help highly useful insights make better business decisions.
Machine learning engineers must know how to work with data. Of course, not to be confused with a data scientist.
Data scientists collect, store, and analyze data to uncover critical insights and findings. Both work together to come up with essential solutions for an organization.
In contrast with a data scientist, a machine learning engineer’s work heavily depends on programming.
Common Job Duties
Let us dig a little bit deeper when it comes to your responsibilities as a machine learning engineer.
After that, we will discuss some of the critical skills you must have to become an ml engineer.
So, here are some of the everyday job duties and roles of a machine learning engineer:
- Developing predictive models by understanding the company’s goals and objectives.
- Being part of or managing the machine learning or AI department to make sure that they run smoothly. Keeping track of milestones and progress to make sure the team meets the deadline.
- To analyze numerous amount of ML algorithms to find solutions to complex problems for the company.
- Extracting, cleaning, and processing data to feed into machine learning models.
- Uncover useful insights from data through the means of data exploration and visualization techniques. Identifying and comparing data distribution to select the best-fit match for deploying machine learning models.
- Verifying the quality of the data to make sure they are suitable for feeding into machine learning models.
- Test and validate machine learning models through numerous amount of experiments.
- Staying up to date with machine learning libraries that already exist and also extend them to find solutions to complex problems. Utilizing these libraries to build, test, and deploy models.
- Build and write programs that enable machines to make decisions without being manually programmed.
These are just some of the everyday job duties of an ml engineer.
Just like a data scientist, a machine learning work involves heavily on data. However, not only data but machine learning engineers are also highly skilled at programming. Since they build, test, and deploy models.
Top 5 Technical Skills of a Machine Learning Engineer
Here are the essential skills on how to become a machine learning engineer:
Programming
Machine learning engineers must know how to code. Their work heavily depends on programming.
Python is one of the most widely used programming languages for machine learning. It comes with already built libraries and frameworks to build models and deploy them.
Knowing how to work with R will also allow you to analyze, manipulate, and understand data to feed into your model.
You should not only know how to program but also understand how to apply object-oriented programming skills, algorithms on data to uncover insights that can help you to create a better model.
As machine learning depends on working with data, companies today know the importance of data. That is why they want someone on board with proper programming skills to write algorithms and uncover useful findings.
These findings help engineers build better predictive models to understand customers, users, and stakeholders.
As a result, having a solid understanding of programming is crucial for every machine learning engineer to become successful in their field.
Although, remember that coding is only an aspect of machine learning. It is more than just getting paid for programming.
Recommended: Coding for Beginners — Start Here
Statistics
You must have a decent understanding of statistics.
You have to learn some of the basic statistical concepts. This includes Regression, Probability Distributions, Statistical Features, Bayesian Statistics, Hypothesis Testing, etc.
In the world of machine learning, building predictive models requires a good understanding of descriptive statistics, probability theory, and regression.
Statistical methods involve exploratory data analysis, data summarization, and data visualizations. As a result, ml engineers can dig deeper into their data to understand patterns of their data and prepare it for better modeling.
A significant element of building a predictive model is to evaluate and come up with the right learning model.
Evaluating a predictive model is known as the experimental design. This design is another useful statistical methods for building machine learning models.
Machine Learning
A machine learning engineer needs to have a solid understanding of how a machine learns.
It is crucial to have the skill to train, test and validate data. And have knowledge of building predictive models using those data.
Big corporations such as Netflix, Google, Uber, and more provide products and services that are data-driven.
That is why they are always in need of individuals that know how to build predictive algorithms using data.
Some of the essential machine learning algorithms you should be familiar with are Linear Regression, Logistic Regression, Decision Tree, SVM (Support Vector Machine), Naive Bayes, etc.
Read: What is Machine Learning? – Supervised & Unsupervised
Mathematics
To build efficient machine learning models, I recommend having a basic knowledge of mathematics. Concepts such as Linear Algebra, Probability Theory, Statistics, and Calculus will come in handy.
Before you get concerned, you do not have to be a math genius to become an ml engineer.
The good news is that the majority of the machine learning frameworks already come with all the mathematical concepts already figured out for developers.
So there is very little chance that you may have to create a math equation from scratch to train your model. However, I cannot emphasize how important it is to understand the concepts before you use prebuilt materials.
There are many useful resources and documentation to learn Algebra, Statistics, or Calculus.
It is not necessary to study all the mathematical concepts. But the specific ones mentioned above.
Data Wrangling
The job of a machine learning Engineer is similar to that of a data scientist. Both roles involve dealing with vast amounts of information and require exceptional data wrangling skills to perform complex models on dynamic data sets.
A majority portion of building a model will require you to analyze unorganized, messy, and unstructured data.
Thus, you need to be able to clean, process, and structure messy datasets before feeding them into your model.
If a product is data-driven, then clean datasets can bring a fortune for companies. Thus, ml engineers working for them must possess the skill on how to clean and wrangle complex datasets.
As a machine learning engineer, a lot of your tasks may need you to transform, process, and map unstructured data.
Transforming them into a form that is more suitable to build a predictive model.
Data Visualization
Data visualization is also another skill that a machine learning engineer should have.
It is a crucial skill for every ml engineer.
Through visualization techniques, you can explore datasets to identify patterns, outliers, and unstructured data. Moreover, represent those data graphically.
It is a unique skill to have when you can graphically represent data to a company.
In addition to other skills, companies nowadays want machine learning engineers to have data visualization skills.
They look for engineers who can not only program and build models, but also visualize complex datasets.
Thus, the skill to graphically represent and communicate data is essential for every machine learning engineer. We are living in an age where a lot of companies depend on data-driven decisions.
Some of the data visualization libraries you may look into are Matplotlib, Tableau, etc.
Having a decent knowledge of such frameworks will help you to stand out in the field of machine learning.
And not to mention, data visualization is also one of the fun aspects of machine learning and AI.
4 Steps on How to Become a Machine Learning Engineer
Now let’s talk about the crucial steps on how to become a machine learning engineer.
Step 1: Develop the Skills
The first step on how to become a machine learning engineer is to gather the knowledge and develop the necessary skills of machine learning. You can start with the skills that I have discussed in this article.
Having a degree in Computer Science or a related field can help. But, I do not think it is mandatory.
So, what skills should you focus on developing?
Well, I have already listed the five technical skills of a machine learning engineer in this article. You can start there.
If you are new to the world of programming, then start by learning how to code.
I prefer that you start learning Python, as this is one of the most widely used programming languages for machine learning. There are tons of free tutorials on Python over the internet.
All these may sound overwhelming. However, you do not have to rush or neither learn everything at once.
Start by learning a programming language. And if you are already familiar with programming, then apply your programming skills to create simple machine learning programs.
Take things slow and steady.
Step 2: Apply Your Knowledge
The second step on how to become a machine learning engineer is to apply your knowledge and skills.
You should not only be learning and practicing the skills but applying them through working on small real-world projects.
These projects can be paid or free.
You may find it hard to land paid projects in the beginning.
But, I suggest that you do not stress about getting paid work.
Mabey work on small projects for free to build your portfolio.
You can try out test projects and apply your machine learning skills to them.
There are tons of test or sample projects on machine learning that will brush up your skills.
Search for machine learning programs for beginners on Google. You will get tons of results.
One thing that I highly recommend that you try is to develop a mini-project from open-source data.
If you go to Kaggle.com, then you can find and play around with a vast amount of open-source data to build a predictive model.
You can also try entering data science competitions to compete for prizes. As data science and machine learning are highly related.
Participating in competitions is an excellent opportunity to apply your knowledge.
Moreover, it is a fun way to meet and network with other machine learning engineers.
Apply your skills and knowledge through projects and competitions. Then once you have a decent portfolio, you can showcase your skills to recruiters.
Step 3: Build Your Resume
The third step on how to become a machine learning engineer is to build a professional resume.
Building a professional resume is a crucial step when it comes to becoming a successful machine learning engineer.
Employers will first take a look at your resume when you apply for jobs. You need to showcase the very best of yourself through your resume.
A resume does not have to be perfect. Just identify your machine learning skillset and have it in order.
Here is a list of sections that your resume should cover:
● Contact info
● Professional Summary
● Key Skills
● Professional Experience
● Education
● Projects
● Social Media Profiles
● Interests, Hobbies, Extra-curricular achievements.
To get started, you can also use the following resume template as an example. Thanks to our friends from dataquest.com.
Step 4: Apply For Machine Learning Jobs
After all the steps, it comes down to where you will apply for machine learning jobs. Time to go after that bacon!
By this time of your journey, you should already have an idea of essential machine learning skills.
Maybe some projects to show off your work, and also a professional resume all under your belt.
Now it is time for you to look for machine learning jobs and apply to them.
Here are five of my favorite job search websites where you can find companies that are looking for ml engineers:
- Indeed.com
- Glassdoor.com
- Monster.com
- Dice.com
- SimplyHired.com
You can also try freelance machine learning jobs if you do not want to work for companies as a full-timer.
Some of the freelance platforms where people are actively seeking freelance machine learning engineers:
- Fiverr.com
- Upwork.com
- Toptal.com
- Freelancer.com
- PeoplePerHour.com
4 Personal Qualities of a Successful Machine Learning Engineer
Knowing how to become a machine learning engineer is not enough. You also must have the qualities that will make you successful.
Honesty
Sounds cheesy? Well, you will be surprised to know the number of people that lack this basic human decency.
If you want to be successful in any aspect of your life, then be an honest human being. It is not that hard.
The first and foremost quality of successful machine learning engineers is that they are honest and unbiased.
When this information is in your hand, you have to make sure that you treat the data with full confidentiality.
Most importantly, be honest with the work you do with data.
Not just that, but you also must be reliable with what you do, what you say, and how you handle confidential data.
Whether you are freelancing or working for a company, you must put your best self forward.
You will be able to build a good reputation not only as a machine learning engineer but as a human being.
Life-long learner
Machine learning is an exciting but challenging career. It is continuously evolving. At every step of the way, there is a learning curve.
An ml engineer who is passionate about learning, exploring, and experimenting with new technologies is always a step ahead in this field.
You must always have the will to teach yourself new materials.
One of the qualities of a successful machine learning engineers is that they are always self-motivated.
Team Player
Companies require machine individuals to work in teams.
It is nearly impossible for one person to do all from analyzing, processing data, and creating an accurate predictive model.
So, getting along and communicating with other people in your team is an essential quality of a successful ml engineer.
But being able to communicate and convey the right message to your team is crucial.
Rather than working alone, you have to work with your team. To solve complex challenges related to projects. This sets you on a path to be a leader in your industry.
Problem Solver
Writing, developing, and building models can be very challenging in some cases. Not only that, but you also may have to spend hours in front of your computer just to clean and process datasets.
I think that everyone is naturally a problem solver. All you have to do is dig deep and use your creativity.
A successful machine learning engineer is always ready to take on challenges.
Have a strong will to fix problems. So when there is a big data in front of you, you will push yourself to rip it apart and find the most useful insights and build a super-accurate model.
Where to Start?
You are at the end of this guide! So, well done!
It means that you are serious about educating yourself on how to become a machine learning engineer.
And guess what? I have just the right platform to suggest where you can start learning data science immediately.
Udacity
If you are not aware, Udacity is one of the most popular online video course platforms on the internet.
Their courses are project-based educational credential programs. Also known as the Nanodegrees.
They have a professional Nanodegree program on machine learning taught by industry experts in the field.
In contrast to that, you will also gain real-world machine learning experience. In addition to that, you will have projects to build your portfolio and advance your career in machine learning.
Similar to their other Nanodegree programs, this program comes with:
- Real-world projects in partnership with industry experts.
- Access to personal career coaching sessions and services.
- 1-on-1 mentor to guide students and answering their questions.
- Learning path designed to be flexible for busy individuals.
The ml Nanodegree program is an excellent way for you to build the necessary skills on how to become a machine learning engineer.
Click here to learn more about Udacity.
Conclusion
Hopefully, this gives you an overall idea of how to become a machine learning engineer.
So, it is time for you to stop reading and start taking the first step to pursue a career in machine learning.
Pursuing a career in machine learning opens doors to a great deal of opportunity.
A path filled with mystery, excitement, and challenges.
Also, expect jobs to rise in the coming decade.
More and more companies are looking for individuals that can help them to make data-driven decisions through predictive modeling. And that is why ml engineers are in such demand.
Are you still wondering on wow to become a machine learning engineer? Not sure where to start? Comment below.
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