What Does it Take to be a Machine Learning Engineer? A Willingness to Fail
Does a career in Machine Learning seem appealing to you? The machine learning engineer field entered the scene when software engineering got cozy with data science. While the role of the data scientist feels similar to machine learning engineer, the two are not the same. However, in order for an ML engineer to do their job, they need data and models from a data scientist. Then, the magic of scaling out the data begins.
EDUCATION AND TRAINING THAT LEADS TO MACHINE LEARNING
While it may seem like there are limited tickets into the field, the truth is somewhat in the middle of that thinking. Some ML engineers take the education path, but others go for the experience road that is a little less traveled. While it’s impossible for an ML engineer to be proficient with every language, tool, or concept, there should be a theme in the ML engineering field that you tend to follow.
So, when it comes to themes, you have to take your cues from both data science and software engineering. Your coding and programming knowledge should include Python, R, Java, and C++. Then, of course, you’ll need everything from statistics and mathematics to data structures and algorithms. Cloud computing, machine learning frameworks, model optimizing and validating, and so much more.
SMALL POOL, LESS COMPETITION
There’s a reason why the pool of candidates is small. On the job experience takes time to build, but the needs within the field will only continue to grow. One of the most important attributes of a ML engineer is a willingness to fail. It’s a relatively new field, and buying into the false notion that a good developer creates perfect code is a recipe for actual failure. The ML engineer field is in growth mode, so it’s important to not be afraid of making mistakes. With an interdisciplinary field, it’s only natural that you won’t be the best coder in the world. Edison didn’t get a lightbulb on his first try. When it comes to ground-breaking work, you have to be willing to push the boundaries on current reality. If you have the technical proficiency, love data and mathematics, and want to be in a career that is making the rules as it goes, a career in machine learning might be the right path for you.
