It’s hard to imagine a world without machine learning. From the government to medical services to e-commerce businesses, machine learning has become a critical component of how businesses and organizations operate.  

Machine learning is rooted in pattern recognition, and those with jobs in this field take on important responsibilities. A machine learning engineer uses communication and analytical skills to create a bridge between the challenges of modern society and the technology that can solve them. Mastering the theory and tools for this profession may happen in and out of the classroom. One option is to pursue a master’s degree program in data science or a related discipline before entering the workforce. 

The problems of the world are complex, and their solutions often call for an in-depth understanding of the systems and technology available to us. That’s where machine learning comes in. So, what is machine learning?

Often considered a subset of artificial intelligence, machine learning is the process of developing and running software that allows computers to learn from data sets. Computers can run programs that read hundreds of thousands of key data points and identify areas of interest much faster than the human eye. Data scientists and AI researchers can then extract the findings from these programs to solve problems and make decisions about the data faster and more efficiently. 

How machine learning works is by teaching a computer program to process information in the same way people do, using knowledge from past experiences to make decisions about new information. 

Machine learning helps people solve complicated problems more efficiently than they can do on their own. 

Sifting through vast troves of information, machine learning software eliminates some margins of human error and requires minimal human supervision, meaning people can spend more time focusing on problems that require human nuance. Because of the increasing and versatile use of technology in a variety of industries, the benefits of machine learning are shaping the future of health care, finance, education, retail and more. 

Because the two are so closely related, machine learning and AI are often used interchangeably when discussing big data and analytics. But what is the difference between artificial learning and machine learning? The latter is the process of computers learning and improving their knowledge. Machine learning is one of the several methods used to achieve artificial intelligence, which is a broader term to describe the science and engineering methods used to teach computers to complete tasks similarly to human intelligence capabilities.

A machine learning engineer is a type of data scientist who specializes in developing software that identifies patterns and analyzes data. They think critically about systems and patterns and use computer programs to solve human problems. 

Machine learning engineers create and develop software that communicates with other computer systems to organize and analyze data sets or perform specific functions. They work on teams with data scientists and data analysts to create programs that run autonomously or with minimal supervision.

Machine learning engineers are employed by consumer-facing companies that provide computer systems design and related services—Netflix and Amazon are just two examples. Machine learning engineers may also find work in a variety of industries including manufacturing, health informatics, marketing, scientific research, retail forecasting, financial services and defense intelligence. 

Data science vs. machine learning—what are some of the differences between the two? In short, machine learning engineers build software to organize and analyze data for other processes or applications. A data scientist, on the other hand, deploys the software created by machine learning engineers to extract insights from data that can help achieve a goal or solve a problem. 

The outlook for machine learning engineer jobs is dependent on a combination of factors, one being location. From 2017 to 2022, the machine learning market in the Asia Pacific region is expected to experience the fastest compound annual growth rate globally, according to Research and Markets. As mentioned, machine learning jobs can be found across multiple industries—this too, may affect demand.

Machine learning engineers are key members of IT and data science teams, and these teams are scaling their capabilities and scope of work. In fact, according to Udemy’s 2020 Skills of the Future report [PDF, 8.8 MB], machine learning is among the five most popular tech skills in today’s marketplace. 

The BLS does not report on machine learning engineer salaries. However, it does provide pay data for people with data science and mathematical science occupations. These professionals earned a median annual salary of $98,230 in 2020. 

Through hands-on experience in data science and software engineering, a machine learning engineer expands their knowledge base to become well-versed in new technologies, big data, statistics and other areas relevant to their work. With a master’s degree in data science, one can learn the ins and out of the field with guidance from professors. They can also collaborate with student peers and explore opportunities to conduct research. 

Becoming a machine learning engineer can take about 18 months to two years, given the time it takes to earn a degree and learn the relevant engineering skills. 

Here are some common steps aspiring machine learning engineers may consider when thinking about how to get started with machine learning:

A machine learning degree or related degree may be a pathway to a career in the field. Syracuse University offers a unique online MS in Applied Data Science curriculum that uses a holistic approach to embed machine learning theories and skills throughout the program. The degree combines skills from information science and management. Aspiring machine learning engineers will have the opportunity to customize their courses, dive deeper into data techniques and explore the business impact of data science in various industries. Graduates can also earn a Certificate of Advanced Study in Information Security Management or Enterprise Technology Management without taking any extra classes.