What Is the Role of a Machine Learning Engineer?

Machine Learning engineer is the most-in-demand profession in the US. Here’s a depth look into the role of Machine learning engineer and also the reason for job’s increase in demand.

More than programming: 

If we say an ML engineer’s job is similar to a computer programmer then it is a dichotomy. 

A ML engineer’s task is to develop a machine by using some algorithms to perform the tasks. Computer programmers generally take the set of rules and data that are predefined before and they will find solutions. Meanwhile a ML engineer takes the solution and data and then converts them into rules.

ML engineer work is closely related to data scientist and software engineer. ML engineers have to derive data from the models defined by data scientists and train the machine to understand commands. The user interface for operating the machine will be created by software  engineers.

Necessary skills : 

The skill set found in data scientists and software engineers are probably related to ML engineers. Usually these skills can be graduated by college and if some skills being missed then they’ll learn these skills once after they stepping into career. 

1) Soft skills –  Soft skill is a non technical skill which is mandatory for all types of engineers. It simply defines time management, business knowledge and iterate ideas. 

2) Basic technical – A ML engineer has to know the intermediate level of python, c++, Math ( statistics, linear algebra and calculus). And also have knowledge on basic physics and Numerical analysis.

3) Subdisciplines – ML engineers to have an idea about computer vision, Natural language processing, voice and audio processing, reinforcement learning.

4) Machine learning/ Neural network concepts – An engineer must learn about neural concepts and learning algorithms

Conclusion: 

The capability gap among software engineers and data scientists can be filled by Machine learning engineers. If these disciplines work together they can make the impractical things as practical and impossible things as possible.