When we are considering the increase in its job postings from 2014 to 2017, there’s no doubt that it increases in popularity as well. Nowadays, many organizations are getting ready to incorporate machine learning in their business. The good news is that not all organizations have been there.
This means that we can still see a lot of opportunities to become a machine learning engineer in a variety of fields. For this reason, pursuing a career in machine learning can be a good choice. But, what should we do to become a professional engineer in machine learning? Read on to find the answers.
First of all, let’s learn what ML engineers are. In general, they are sophisticated programmers that create machines and systems. The machines here can learn and apply the knowledge they get without particular direction. Different from traditional computer programmers, they are focused on programming machines to perform particular tasks.
A machine learning engineer develops programs which enable machines to do something automatically. One of the examples is the creation of a self-driving car. But, there are other applications for the work of machine learning. And being ML engineers allow you to jump to various industries.
Before becoming an ML engineer, it is important to understand the ecosystem first. Once you become an ML engineer, you should understand what you are designing for. For example, you are creating a program for a grocery chain. Then, the company asks you to issue targeted coupons regarding past purchase history.
Thus, you probably need to write an automatic coupon generation system. And to write this system, as a machine learning engineer, you should understand the whole ecosystem including catalog, inventory, purchase orders, pricing, bill generation, and other things. As a result, you can have working software.
Then, what can we do to become a professional ML engineer? Considering its job description, you need to be technically skilled to pursue a career in machine learning. Most machine learning positions will require the use of C/C++ or Python. You will work with various algorithms and apply them to different settings too.
One of the easiest ways to pursue this role is to start with a background in software engineering. Then, continue to learning statistics and machine learning knowledge. To become a machine learning engineer, you can also teach yourself both the machine learning basics and software fundamentals.
However, it can be difficult since there is still stigma related to people who are self-learning in a particular industry. If you think you need some professional guidance, you can take a course in machine learning or join a boot camp which offers a job guarantee.
You can also consider taking a master or doctoral degree in a relevant discipline to pursue this career. Fields of study include mathematics and computer science. You may need to have experience in computer programming to get the role. Having specific programming languages will be an advantage.
Now, let’s get into the details of what you need to become a professional machine learning engineer. Computer science fundamentals are among the most important skills for ML engineers. It includes knowledge in data structures, algorithms, computability, and complexity, as well as computer architecture. And here are the other essential skills needed.
A formal categorization of probability as well as techniques gained from it becomes the key of many machine learning algorithms. Closely connected to this knowledge is statistics. This field offers a range of measures including median, variance, mean, and others. Many ML algorithms need a good understanding of this field too.
Data modeling refers to the process of guessing the underlying structure of a specific dataset. It may include a goal of discovering meaningful patterns. As a machine learning engineer, you need to understand this process. Understanding an evaluation strategy is also needed even for applying simple algorithms.
Since the typical output of an ML engineer is software, you need to know how to put everything together. In this case, you should know how to use database queries, library calls, and others. You may need to learn further about software engineering practices like system design, version control, documentation, testing, etc.
Besides, you should know how to apply machine learning algorithms and libraries as well. Standard applications of ML algorithms are broadly available through packages or APIs like TensorFlow, Spark MLlib, Theano, Scikit-Learn, etc. You should understand how to apply them effectively as a professional machine learning engineer.
You have already known a few important things about ML engineers. But, do you think the roles of an ML engineer are similar to a data scientist? At a glance, their roles may look similar. But, when they are working on the same team, you will know that they do different things.
A data scientist typically does the statistical analysis needed to decide which machine learning approach to apply. Then, they also model the algorithm and prototype it for examining. After that, a machine learning engineer will seize the prototyped model and make it work. Is there another difference?
Yes. An ML engineer isn’t essentially expected to know the predictive algorithms and their basic mathematics like a data scientist does. However, an ML engineer is supposed to master the software tools which make the algorithm functional. So, it can be concluded that they are different.
In conclusion, being an ML engineer can be a good role to take nowadays. However, to become a professional ML engineer, you need to acquire various skills including programming, statistics, probability, system design, and software engineering. So, are you interested to become a machine learning engineer?