Machine Learning Engineers are individuals who are experts at turning machine learning algorithms into working software products, either stand alone or as part of a larger software ecosystem. In many ways their role is a combination of a data analyst and a software engineer. They perform statistical analysis and use predictive algorithms like data analysts, but their audience is software with minimal human supervision, rather than executives and key stakeholders.
Machine Learning Engineers are one of the more recently developed data science roles and opportunities in this area have rapidly been expanding as businesses become more willing to effectively incorporate machine learning into their business structures and products.
What You Need To Know
Machine Learning Engineers differ from other Data Scientists mostly in the depth of their knowledge of software design and development processes. Most have an undergraduate degree in either computer science or statistics and a graduate degree in the other, but some are able to develop the additional required skills from a single one of those bases while in a professional position.
From the computer science perspective, it is important to know data structures, computer algorithms, computability and complexity, and computer architecture. This knowledge is used as the basis for learning software development skills, specifically how different pieces of software work together and fit into a larger environment. This extends to understanding best practices, so you can understand and build on existing software design knowledge rather than reinventing the wheel in a costly and difficult way.
If you are in a more statistical role, encountering statistical equivalents of these items is likely. Lists, data frames, and vectors are more flexible forms of data structures, learning computer algorithms will be pretty straightforward after learning statistical ones, but dealing with how to solve problems of computability and complexity and the important aspects of computer architecture will likely require deeper study. From the statistical perspective, it is important to understand probability and techniques derived from it, summary statistics, distributions, and analysis methods.
Computer science curriculum and practice is much less likely to touch on statistics than statistical programs are to touch on computer science unless you actively seek it out. These are used as the basis for learning how to identify useful patterns and building predictive analytics. Just as importantly you learn how to evaluate models to determine if a given model is providing an accurate pattern or prediction and the proper ways to refine the model to repetitively improve overall accuracy.
This knowledge and information is combined to allow for building true machine learning software products, using expert knowledge of both computer science and statistics as well as where they intersect.
What You Will Be Doing
Machine Learning Engineers are responsible for understanding the entirety of a company’s environment in a way that other positions are not. Analysts of various sorts are able to just understand the parameters of the specific problem they are working on in order to produce targeted analysis, and data engineers mostly just need to understand the data infrastructure, but machine learning engineers need to understand how each piece fits together. They need to know both how each piece of the business leads into whatever problem they are trying to solve as well as how the company’s software environment pools and utilizes information related to that same problem.
The bulk of your time will be spent:
- Getting the business knowledge required to design your solution,
- Learning the software or architecture knowledge required to implement your solution
- Building your solution
- Interacting with project and product managers to make sure that your solution will fit into the framework of internal and external products your organization produces
Because your expertise will eventually, naturally expand to include everything that your organization does, it is not unusual for machine learning engineers to be in the room during executive or management meetings, helping provide their particular insight into what is going on with a particular area of the company, or of worthwhile strategic decisions that the company can make. This access naturally leads to interesting opportunities in career development.
Machine learning engineers have a lot of potential to become more important within the context of their current organization. Their subject matter knowledge will naturally blossom to include most everything their company or division does making them natural choices to move into leadership positions in the analytics, software, or even business groups.
Additionally, there are many industries where machine learning is not adopted and utilized, giving plenty of opportunity for enterprising machine learning engineers to help carve out a space for their particular skill set as an industry innovator. Regardless, there is plenty of growth potential for a machine learning engineer who is willing to work hard and take advantage of opportunities as they emerge. To learn more on how to start your career as a machine learning engineer, click here.