All You Need to Know about Machine Learning Engineers in 2024
Did you know that the demand for machine learning (ML) engineers is expected to grow by 16% between 2020 and 2030, according to the US Bureau of Labor Statistics?
If you are wondering what is causing this surge and what it means to be a machine learning engineer in 2024, you have come to the right place. This article will discuss everything you need to know about machine learning engineers, including their roles, salaries, and more.
What is a machine learning engineer?
These professionals are responsible for designing, developing, and implementing machine learning models and systems that can analyse and make predictions based on data.
Machine learning engineers work closely with data scientists, software engineers, and domain experts to understand specific problems and business requirements.
They play an important role in using data to create intelligent systems and applications that can improve decision-making, automate processes, and drive innovation in various industries.
What does a machine learning engineer do?
They perform the following roles:
Machine learning engineers gather the data they need for training models. They work closely with data scientists and analysts to determine what kind of data is necessary. It can be anything from customer behaviour on a website to readings from a temperature sensor.
Machine learning engineers clean data to make it usable. They remove errors, fill in gaps, and convert the data into a format that machine learning algorithms can easily use. Additionally, they use techniques like normalisation and transformation to ensure the data is ready.
Machine learning engineers select features carefully. They analyse the data to find out which elements are most useful for prediction. They may also combine two or more features to create more informative ones.
They evaluate different algorithms to see which one suits the problem the best. They consider various factors such as accuracy, speed, and complexity to choose the right algorithm.
They feed the collected data into the model that learns to make predictions or decisions based on the given data. They also monitor the training to ensure the model is learning effectively.
They use different data sets to evaluate how well a model is performing. They look at metrics like accuracy, precision, and recall to gauge its effectiveness.
They also use techniques like hyperparameter tuning and ensemble methods to optimise the model. This helps increase its accuracy and efficiency.
Machine learning engineers work on deploying the model into a production environment. It includes integrating it with existing systems and software.
Monitoring and maintenance
Once the model is live, it’s important to keep an eye on its performance. Hence, they regularly check to see how the model is doing. They update the model as needed, especially if new data becomes available.
Documentation and reporting
Clear documentation is essential for any machine learning project. Therefore, machine learning engineers document their work so that others can understand it. Documentation also makes it easier for team members to collaborate and achieve results within the assigned time.
What skills do machine learning engineers have?
Machine learning engineers possess these skills:
They are proficient in programming languages like Python or R. It helps them implement algorithms, manipulate data, and build models. It also allows them to troubleshoot issues and make updates easily.
They are able to inspect, clean, and make sense of raw data. They also have an understanding of statistical measures, distributions, and data visualisation techniques that help them get useful insights.
Machine learning algorithms
They are familiar with different machine learning algorithms and know when to use them.
They know how to prepare and clean data to create effective machine-learning models. They also know how to handle missing data, detect outliers, and normalise features.
They have the skills required to understand metrics like accuracy, precision, recall, and F1 score. They help them optimise their models and ensure that they are aligned with business objectives.
They know how to communicate complex technical concepts in a simple way. Good communication helps them have a smooth collaboration with data scientists, business analysts, and other stakeholders.
Engineers understand the effects of biassed data and strive for fairness and transparency.
How to be a machine learning engineer?
Here is a simple step-by-step to become a machine learning engineer:
Step 1: Acquire a strong educational foundation
You usually need at least a bachelor’s degree in computer science, data science, or a related field. Focus on subjects like mathematics, statistics, and programming during your education.
Step 2: Learn programming languages
Being proficient in programming languages like Python or Java is crucial. Many online tutorials and courses are available to help you learn these languages.
Step 3: Take specialised courses
Once you have a good understanding of programming and basic computer science concepts, you should start taking specialised machine learning courses.
Step 4: Gain experience
Work on your own machine learning projects or collaborate with others. This will help you apply what you have learned and better understand how to solve real-world problems.
Step 5: Master machine learning tools
Learn how to use machine learning libraries and frameworks like TensorFlow or scikit-learn. You should also become familiar with data manipulation libraries like Pandas.
Step 6: Build a portfolio
Create a portfolio to showcase your skills and knowledge to potential employers. Include detailed explanations of each project, the problems you solved, and how you did it.
Step 7: Apply for entry-level jobs
Look for positions that allow you to use machine learning techniques, even if the role is not exactly for an ML engineer.
How much do machine learning engineers make?
According to Glassdoor data, the average salary of machine learning engineers is around $123,101 per year in the United States. It consists of an average salary of $106,954 per year and an additional salary of $16,147 per year.
A day in the life of a machine learning engineer
A day in the life of a machine learning engineer starts with a quick review of important emails and messages. Then, they move to coding. They spend a lot of their time on data pre-processing.
By midday, they mostly work on model building and refinement. This could involve selecting an appropriate machine learning algorithm and setting it up to run on a prepared dataset.
The afternoon could involve meetings with other professionals like data scientists, business analysts, software developers and stakeholders.
Before wrapping up, they may review the whole day’s work and create a to-do list for the next day.
Interesting stats about machine learning
These amazing stats will show you the value of ML engineering and why it is the right time to join this field.
- Machine Learning market size was around $158 billion in 2023. It is expected to reach $528 billion by 2030. [Source: Statista]
- The US tops the global machine learning market at $56.75 billion. [Source: Statista]
- Around 49% of companies in the world are using or planning to use machine learning. [Source: McKinsey]
- Technologies in machine learning and artificial intelligence may boost global GDP by 14% by 2030. [Source: WSJ]
Is machine learning a good career?
Yes, machine learning is considered a good career due to its high demand, lucrative salary, and career opportunities.
Can a data scientist become a machine learning engineer?
Yes, a data scientist has a strong foundation to shift into a machine learning engineer role. Both positions require a deep understanding of data manipulation and statistical analysis.
However, a data scientist may need to strengthen his/her programming skills and get more familiar with machine learning algorithms to switch effectively.
Can a software engineer become a machine learning engineer?
Yes, a software engineer can become a machine learning engineer. However, they will need some additional skills, such as statistics, data analysis, and machine learning algorithms for this.
How can you become a machine learning engineer without a degree?
Becoming a machine learning engineer without a formal degree is challenging but not impossible. You will need to make use of online resources like tutorials, courses, and MOOCs that cover essential topics in programming, data science, and machine learning.
Working on personal projects can provide you with practical experience and a portfolio to showcase your skills. You may also benefit from internships or freelance opportunities that offer hands-on experience.
Can machine learning engineers work from home?
Yes, machine learning engineers can work from home. The nature of the job often requires a good computer and a stable internet connection, which many engineers already have. However, some companies may also require occasional in-office meetings.
Summing it up
The role of a machine learning engineer in 2024 and beyond is more important than ever before. With the growth of data, the need for skilled individuals who can transform raw information into actionable insights has become high.
To thrive as a machine learning engineer in today’s data-driven world, you must stay curious and adapt to emerging technologies and trends.