The role of engineering in machine learning

What’s the first thing that comes to mind when you think of engineers? Perhaps it’s a vision of someone in a hard hat helping to build the infrastructure of tomorrow – whether it be buildings, bridges, or highways.

For many of us, engineering brings up a romantic view – of someone working on things that help our economy tick along. While it’s true that engineers can work on big projects, you may be surprised to learn that they are often also significant contributors to the design and development of data centres – a central tenet of modern data engineering.

For engineers, a qualification such as a Graduate Diploma in Data Science can help refine their skills further and provide them with the best possible start to roles such as machine learning (ML) engineers. Let’s discover how the skills that engineers learn can be readily repurposed for use in one of today’s fastest-growing industries.

Engineering: More than construction

Engineering is a field often defined by incorrect assumptions and perceptions. Many people lack an understanding of what an engineer does, incorrectly assuming that engineering roles focus solely on construction problems – from bridges to buildings and beyond. In reality, a career as an engineer is far more diverse than the big construction projects you may see on TV. So, what does an engineer do?

In reality, engineers form a much more diverse field of problem-solving professionals. Engineers are problem-solvers who are heavily involved in developing systems, products, machines, and structures. Using scientific research and findings, they apply this knowledge to develop solutions – whether using new knowledge to improve the efficiency of existing systems or developing products that help contribute to a larger overall project.

Depending on an engineer’s particular skillset, they may be involved in developing solutions to some of the world’s biggest challenges, which aren’t necessarily things ordinary Aussies see every day. Consider, for example, the infrastructure required to keep the Internet operational – something that seems as simple as an IP address often has required the work of engineers.

In engineering, two types of engineers work heavily with computers and computer systems: software engineers and electrical engineers.

Software engineers are the type of engineers involved in developing software and programs – solutions that, by design, are heavily immersed in a modern, digital world. These engineers often form part of development teams, helping to contribute to the creation of well-defined software solutions and maintenance post-release.

Electrical engineers, on the other hand, are involved in the development of physical infrastructure – in particular, those involving electrical systems, from systems as large as power plants to as small and complicated as the fabrication of the computer chips that software engineers use every day.

An emerging field: Machine learning

In today’s increasingly data-dependent world, engineers are facing new challenges. Take, for example, the sheer amounts of data generated by systems large and small. In a world where there are not enough data analysts, engineers are being called upon to help simplify and streamline some of the challenges that exist for businesses.

Take, for example, machine learning. A field of computer intelligence, machine learning involves developing and using computer systems to create models that can learn and adapt without instructions, typically through statistical models and other solutions. To develop machine learning solutions, one must have skills and knowledge spread across multiple fields – typically, understanding the nuances of large-scale data sets and having the technical experience to create well-defined, efficient solutions.

Applications of machine learning

With the advent of big data and continued drops in computing costs, various opportunities for machine learning engineers have opened up across multiple industries. These opportunities hope to tackle some of the problems that big businesses face on a daily basis and aspire to transform the way we work, often for the better.

Consider, for example, the large amount of work done to process and apply home loan applications. In financial services, a multi-billion dollar industry, much of the work involved in home loan applications involves manual data handling and data entry – from payslips to bank transaction records. Machine learning can help tackle some of these problems – with algorithms enhancing past work, such as optical character recognition (OCR), to rapidly reduce the time it takes to process customer data. In turn, this can help to reduce loan application times, helping customers understand their borrowing capacity in a more timely fashion.

Machine learning has uses across many industries, with machine learning engineers in demand in industries as diverse as consumer retail, healthcare, financial services, and transport. With rapid data growth comes a requisite increase in demand, with one industry monitor projecting that by 2030, machine learning applications will be worth more than $500bn USD worldwide.

Machine learning: A unique opportunity

The rapid growth of machine learning presents a unique opportunity for engineers – the ability to pivot into a career that is not only in high demand but also tackles some of the most significant challenges of our time.

For engineers, machine learning presents an opportunity to hone their craft in a diverse and unique field, enabling them to enhance their subject matter expertise in an area that is almost certain to be in high demand in the years to come. For students studying data or engineering, an opportunity exists to specialise in a new and emerging field that will pose unique challenges for even the most curious graduates.

There are many reasons to consider becoming a machine learning engineer. For some, it’s the salaries on offer, particularly in roles that require minimal experience. For others, it’s the ability to use new and emerging technologies to help create cutting-edge solutions that make a meaningful difference in many lives.

Ultimately, a career in machine learning offers many unique opportunities to hone your craft. With a variety of challenges to tackle, it’s sure to keep even the most inquisitive engineers on their toes.

If you’re interested in pursuing a career as a machine learning engineer, you should talk to a careers advisor and learn about your options. Hopefully, today’s exploration of how engineering can lead to opportunities in this new and emerging field has highlighted some new opportunities to explore.