Recently we discussed how to get a job in artificial intelligence and machine learning, and here we’ll explain what a machine learning engineer does. We’ll cover the common job titles, work duties, and daily responsibilities of machine learning engineers.
First, it’s important to recognize that few industry professionals hold the official job title of “Machine Learning Engineer.” Even machine learning experts who handle essentially the same responsibilities may end up holding very different job titles, including:
- Machine Learning Engineer
- Machine Learning Scientist
- Machine Learning Data Developer
- Software Developer
- Computational Scientist
- Computer Systems Designer
- Computer and Information Research Scientist
And while the specific responsibilities for the jobs listed above certainly can and do vary, anyone working in the field of machine learning is likely to have at least some overlap in their responsibilities.
As we explore what machine learning engineers do, we’ll also review whether or not machine learning is a good field to launch a career in, we’ll give you advice on how to prepare yourself to find success in the industry, and we’ll explain how to pick the right school if you do choose to pursue a machine learning degree.
When you’ve finished reading this post, fill out our information request form to receive additional details about CSU Global’s 100% online Master’s Degree in AI and Machine Learning, or if you’re ready to get started, submit your application today.
Typical Machine Learning Roles & Responsibilities
Machine learning engineers play a vital role in today’s economy, and especially in industries where artificial intelligence and machine learning technology have already begun to impact best practices.
And while machine learning engineers in various roles may be responsible for dramatically different things, there is certainly a common set of core responsibilities that anyone looking to break into the industry will need to develop a proficiency in, including:
- Using AI reasoning and uncertainty to set and achieve complex objectives.
- Applying machine-learning concepts, logic, and probability analysis to solve complex problems.
- Analyzing AI and machine learning-based systems to monitor and control project development.
- Using AI and machine learning best practices for processes like speech recognition, data processing, data mining, and robotic control.
But before we delve into details about the specific work duties and responsibilities of machine learning engineers, let’s first explain the difference between artificial intelligence and machine learning to give you a feel for which specific niche may be a better fit for you.
What is the Difference Between Machine Learning & AI?
Machine learning and artificial intelligence are closely related disciplines, each of which uses technology to help make machines smarter, allowing them to mimic human activity or thought processes.
The goal of each field is the same, helping machines do a better job of solving complicated problems or performing complex tasks.
However, there is a critical difference between machine learning and AI, and this is clearly summarized in an article from Towards Data Science, which defines the two fields as follows:
- Machine learning uses classical algorithms to complete complex tasks, like clustering, regression, or classification projects, and the algorithms driven by machine learning processes must be trained on data before they’re able to operate effectively. The more the algorithms are trained, the better they’re able to complete the tasks they’ve been assigned.
- AI is a term that’s used broadly and applies to any applications where a machine attempts to mimic human cognitive functions, like learning or problem-solving. AI systems can get incredibly complex, but they can also be as simple as a series of nested if-else statements.
If you’ve been paying attention to the economy over the past several years then you’re likely aware that both machine learning and AI-driven solutions have already been applied to a wide variety of tasks.
In some cases, machine learning and AI solutions are even on the verge of revolutionizing processes, systems, and entire sectors, like transportation.
Which industries have seen the heaviest impact from machine learning and AI technology? According to Robotics Business Review, the fields that have been most heavily impacted by these new disciplines are:
- Entertainment - Machine learning technology is being used to review user behavior, predict demand, then customize suggestions for movies, music, TV shows, advertisements, and more.
- Healthcare - Machine learning and AI solutions are being used to power autonomous surgical robots, virtual nursing assistants, automated image diagnosis, and reduce dosage errors.
- Manufacturing - Machine learning and AI-driven robots have been used for years to increase profit margins by improving efficiency and accuracy, reducing waste, down-time, and cost overruns.
- Sports - Machine learning automation and predictive analyses are being used to help determine important business decisions around sponsorship activations, ticket sales, and even athletic performance.
- Transportation - Machine learning and AI technology have been integrated into self-driving cars, and widescale testing is now underway to literally remove the human component from the act of driving, providing truly autonomous vehicles.
Obviously, machine learning and AI technology are having a significant impact on certain sectors of the economy, and this is precisely why machine learning engineers and AI experts are so important to the performance of certain initiatives and organizations.
Now that you’ve got a better idea about what machine learning actually is, what AI is, and how they’re different, let’s take a look at some of the most common tasks assigned to machine learning engineers.
What Do Machine Learning Engineers Actually Do?
Machine learning engineers are often tasked with developing new applications and processes to improve performance by reducing waste and inefficiency.
The technology of machine learning can be applied to all sorts of different processes, and virtually anything that you can train a person to do can be taught to a machine, especially when combining both AI and machine learning strategies.
To launch a successful career in machine learning engineering, you’ll want to develop specialty knowledge and experience in a few critical areas, including:
- Understanding, analyzing, and applying AI and machine learning principles for reasoning processes and uncertainty.
- Utilizing machine learning and techniques to perform image analysis and reconstruction, along with other similar tasks.
- Solving a variety of complicated problems and scenarios by implementing machine learning and AI-driven solutions.
- Designing, then building machine learning and AI-based solutions to perform complex tasks, modeling, or improving upon typical human behavior.
- Devising and building effective solutions that integrate both machine learning principles and AI best practices to solve complex problems.
- Evaluating and improving applications that rely on machine learning and AI processes.
As you might imagine, machine learning engineers need to be problem solvers, deep thinkers, and effective experts at applying machine learning best practices.
What important to understand here, however, is that machine learning isn’t an easy discipline that you can simply figure out on your own.
Instead, this is a complicated field that requires a significant investment of time and resources, as anyone who hasn’t developed the foundational knowledge, skills, and techniques that the discipline requires is unlikely to find much success in the industry.
This is why it’s so important that you study machine learning in a serious academic setting; you’ll be able to address the most common challenges that machine learning engineers face on a daily basis.
Is Machine Learning a Good Career?
Yes, the field of machine learning offers an excellent opportunity for anyone who’s looking to lead the next generation of computer professionals using cutting-edge technology.
The machine learning industry is also projected to grow incredibly quickly over the next several years, with a recent Forbes article detailing that the global machine learning market is expected to grow by 44% between 2017 and 2024, reaching $20,83B in 2024.
Accordingly, there’s never been a better time to get into the industry, but as we mentioned earlier, the best way to do that isn’t to simply try and figure out machine learning processes on your own but to study them in a serious academic setting.
If you really want to work in machine learning, then you should consider earning an accredited Master’s Degree in AI and Machine Learning from CSU Global.
How Can You Get Into Machine Learning?
While you don’t necessarily need a degree in machine learning to land a job in the field, holding the right academic credentials is likely to make it easier to get a good job.
This is especially true if you want to work at a traditional organization, and doubly so if you’re looking to get a role in leadership, management, or doing anything other than entry-level work.
Should you choose to get a Master’s in AI and Machine Learning from CSU Global, you can rest assured that you’re doing the right thing, as our program will help you to develop the foundational skills and industry experience needed to thrive in roles that require advanced knowledge of programming, software development, AI, and machine learning best practices.
Completing our AI and Machine Learning Master’s program is one of the best ways to prove that you’re prepared to provide value to any organization from day one, so if you’re serious about getting into machine learning, then you should seriously consider enrolling in our online M.S. in AI and Machine Learning program.
What Jobs Can You Get With a Master’s Degree in Machine Learning?
CSU Global’s online Master’s Program in AI and Machine Learning will provide you with everything needed to secure an excellent industry role, including detailed experience in machine learning processes like:
- Deep-learning libraries, such as Tensorflow.
- Experience using machine learning solutions to solve complex problems in healthcare, manufacturing, oil/gas, and automotive.
- Proficiency in Python programming.
You’ll graduate from CSU Global’s online M.S. in AI and Machine Learning program prepared to open doors in the industry and advance your career, and you may even be able to secure some of the top jobs machine learning jobs, like:
- AI Engineer/Scientist / 2019 Median Pay: $102,521
- Computer and Information Research Scientists / 2020 Median Pay: $126,830
- Software Developer / 2020 Median Pay: $110,140
Keep in mind, however, that this is only a shortlist of a few potential job titles you could pursue with an M.S. in AI and Machine Learning from CSU Global.
In fact, completing your Master’s Degree in AI and Machine Learning will qualify you to pursue a much wider variety of jobs in different industries and at all sorts of organizations, including private companies, nonprofit organizations, and even government agencies.
Why Should You Consider Studying Machine Learning at CSU Global?
CSU Global’s Master’s Degree program in AI and Machine Learning offers the ideal way to develop the skills, abilities, and foundational knowledge you need to pursue top leadership positions in the field of machine learning.
Our M.S. in AI and Machine Learning program is regionally accredited by the Higher Learning Commission and widely respected by industry professionals.
In fact, CSU Global recently received several important rankings, including:
- A #1 ranking for Best Online AI Degree by SuccessfulStudent.
- A #3 ranking for Best Colleges in Colorado from Best Value Schools.
If you want to secure a role leading the next generating of computing using cutting-edge technological solutions, then you should get your Master’s Degree in AI and Machine Learning from CSU Global.
To get additional details about our fully accredited, 100% online Master’s of Science Degree program in Artificial Intelligence and Machine Learning, please give us a call at 800-462-7845, or fill out our Information Request Form.
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