Machine learning seems to be picking up steam as one of the buzzwords to look out for this decade.
Among the U.S. and Japan-based I.T. professionals surveyed in 2017, three-fourths said they were already using machine learning for cybersecurity. Most were also confident that the cyberattacks on their businesses within the past year used machine learning. Despite its increasing use, machine learning remains an ambiguous concept among more than half of the respondents.
Regardless, data has become the new ‘black gold’ in recent years, according to some experts. The entrepreneur in this data-driven economy relies on information derived from collected data to make more informed decisions. It wouldn’t be surprising for a business to invest heavily in software and other solutions built on sophisticated neural networks.
Creating such networks is no easy task. Whether feed-forward or recurrent, a neural network must be capable of learning as it feeds on more data. It also has to learn new things in a period measured in days, if not seconds. By contrast, the human brain takes years for something to become second nature to a person.
Central to this effort is the machine learning engineer. It has grown to become the most in-demand profession in the U.S., with related job opportunities spiking by 344% in 2019. Here’s an in-depth look into the role of a machine learning engineer and the reasons for the job’s increase in demand.
More Than Programming
To say that a machine learning engineer’s job is similar to a computer programmer is a dichotomy. While performing programming to an extent, a machine learning engineer’s task is to develop the machine to perform tasks without being explicitly told.
Computer programming takes rules and data, and then turning them into solutions. Meanwhile, machine learning takes solutions and data, and then turning them into rules. Furthermore, computer programming can develop a general-use calculator, while machine learning can develop one for a specific niche.
Machine learning engineers work closely with data scientists and software engineers. They create control models using data that are derived from the models defined by data scientists, allowing the machine to understand commands. From there, the software engineer designs the user interface from which the machine will operate.
The final product is software, like cnvrg MLOps, combining best practices from DevOps, software development and I.T. operations, and machine learning engineering. Organizations tend to spend more on infrastructure development when a machine learning-ready software can provide a precise estimate on how much they need.
Machine learning engineers have a diverse skill set–with some skills encompassing those found in data scientists and software engineers. It’s usual for one to graduate from college and begin working with some skills missing since they’ll learn these skills as they move up the career ladder anyway.
The necessary skills for machine learning engineering fall under any of the four categories.
- Soft skills – These are non-technical skills necessary to keep up with the fast-paced nature that defines machine learning. An engineer must manage their time properly, possess some business knowledge, and iterate ideas quickly.
- Basic technical – These skills include intermediate-level Python and C++ and some basic-level math (e.g., linear algebra, calculus, and statistics). Some engineers also possess basic physics and numerical analysis knowledge.
- Subdisciplines – Under this group include computer vision, natural language processing, voice and audio processing, and reinforcement learning. Automatic machine learning is an added benefit for engineers.
- ML/NN concepts – Machine learning extends beyond neural networks. An engineer must also learn non-neural network concepts, namely algorithms. Nevertheless, neural networks are still equally important as they’re the most accurate approach to problem-solving.
As mentioned earlier, the end product of machine learning engineering is software. Still, its applications are far and wide–beyond predicting business trends and auto-filling search terms.
For instance, Stanford University’s Autonomous Helicopter Program demonstrates the feasibility of teaching an aircraft flight. Researchers installed a system that uses reinforcement learning on a Yamaha R-50 helicopter. It managed to perform stunts a human-crewed helicopter would have difficulty doing, if not impossible to do, continually correcting its course with each pass.
Similar autonomous technology found its way in the driver’s seat of Google’s self-driving vehicle. Described as ‘on the bleeding edge of artificial intelligence research,’ the car learns from human behavior on the road to drive. While the technology won’t replace human drivers anytime soon, it shows the possibilities machine learning engineering is turning into reality.
It’s safe to say that machine learning engineers fill capability gaps among software engineers and data scientists. When these disciplines work together, they create technologies previously thought impractical or impossible. No doubt that they’re paving the way to the future.